Machine Learning Engineer

Machine Learning Engineer at ProofofSkill · Bengaluru (Bangalore) · 3 - 5 years · ₹28L - ₹30L / yr · Raised funding · Posted 18 Sep 2026

Machine Learning Engineer

at ProofofSkill

Posted by Atharva K

3 - 5 yrs

₹28L - ₹30L / yr

Bengaluru (Bangalore)

Skills

databricks

XGBoost

Python

Machine Learning (ML)

SQL

Machine Learning Engineer (For client company)

Location: Bengaluru, India (Hybrid/Onsite)

Experience: 3–4 years

The Role

We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.

You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.

This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.

What You'll Do

Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
Build training, evaluation, and inference pipelines using Databricks.
Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
Define and own model evaluation metrics for safety-critical systems, including:
Precision
Recall
Sensitivity
Specificity
False alarm rate
Missed event rate
Detection latency
Analyze production model performance across facilities, residents, devices, and time periods.
Handle noisy real-world datasets, including:
Missing values
Label quality issues
Device variability
Sparse event data
Facility-specific patterns
Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
Deploy, monitor, and continuously improve production ML models.
Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

What We're Looking For

3–4 years of experience building and deploying machine learning systems in production.
Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
Strong understanding of classical machine learning concepts, including:
Feature engineering
Model training
Cross-validation
Error analysis
Model evaluation
Hands-on experience with algorithms such as:
XGBoost
Random Forests
Gradient Boosting
Ensemble methods
Anomaly Detection
Time-series models
Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
Comfortable working in a fast-moving startup environment with ambiguity.
Strong ownership mindset with the ability to take ML models from experimentation through production deployment.

Good to Have

Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.

Exposure to:

Computer Vision
Pose Estimation
Skeleton Tracking
Object Tracking
Spatial Data Processing
Experience with:
MLflow
Model Registry
Feature Stores
Experiment Tracking
Model Monitoring
Experience with:
ONNX
Model Quantization
Edge Deployment
Latency Optimization
Resource-Constrained Inference
Familiarity with real-time data pipelines using:
Kafka
Spark Structured Streaming
Streaming inference architectures

Machine Learning Engineer (For client company)

Location: Bengaluru, India (Hybrid/Onsite)

Experience: 3–4 years

The Role

We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.

You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.

This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.

What You'll Do

Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
Build training, evaluation, and inference pipelines using Databricks.
Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
Define and own model evaluation metrics for safety-critical systems, including:
Precision
Recall
Sensitivity
Specificity
False alarm rate
Missed event rate
Detection latency
Analyze production model performance across facilities, residents, devices, and time periods.
Handle noisy real-world datasets, including:
Missing values
Label quality issues
Device variability
Sparse event data
Facility-specific patterns
Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
Deploy, monitor, and continuously improve production ML models.
Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

What We're Looking For

3–4 years of experience building and deploying machine learning systems in production.
Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
Strong understanding of classical machine learning concepts, including:
Feature engineering
Model training
Cross-validation
Error analysis
Model evaluation
Hands-on experience with algorithms such as:
XGBoost
Random Forests
Gradient Boosting
Ensemble methods
Anomaly Detection
Time-series models
Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
Comfortable working in a fast-moving startup environment with ambiguity.
Strong ownership mindset with the ability to take ML models from experimentation through production deployment.

Good to Have

Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.

Exposure to:

Computer Vision
Pose Estimation
Skeleton Tracking
Object Tracking
Spatial Data Processing
Experience with:
MLflow
Model Registry
Feature Stores
Experiment Tracking
Model Monitoring
Experience with:
ONNX
Model Quantization
Edge Deployment
Latency Optimization
Resource-Constrained Inference
Familiarity with real-time data pipelines using:
Kafka
Spark Structured Streaming
Streaming inference architectures

Users love Cutshort

Read about what our users have to say about finding their next opportunity on Cutshort.

Shubham Vishwakarma

Full Stack Developer - Averlon

I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.

Companies hiring on Cutshort

About ProofofSkill

Founded :

2024

Type :

Product

Size :

0-20

Stage :

Raised funding

About

N/A

Company social profiles

Similar jobs (10)

Advanced Machine Learning Engineer

at Ampera Technologies

Posted by Faisal AshrafNomani

Remote only

5 - 10 yrs

Best in industry

Data Science

Machine Learning (ML)

Python

PyTorch

TensorFlow

+3 more

Role Summary

We are seeking an Advanced Machine Learning Engineer to design, build, train, deploy, and operate scalable machine learning systems. This hands-on ML engineering role focuses on implementing models and production systems—not solely conducting data analysis or research. It requires strong Python and deep-learning expertise, experience with frameworks such as PyTorch or TensorFlow, and the ability to own the end-to-end model lifecycle, from behavioral/event data preparation and feature engineering through model development, large-scale training, deployment, inference optimization, and monitoring. The engineer will work with Snowflake, Databricks, Spark, and GPU-based training infrastructure to deliver reliable, efficient, and maintainable ML capabilities.

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps.

Key Responsibilities

Machine Learning Engineering

Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable.
Translate model requirements into robust, reusable, and testable Python components.
Build model training, validation, scoring, and inference workflows that can be integrated into production applications.
Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency.
Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior.

Train models efficiently on GPU infrastructure using techniques such as mixed-precision training, gradient accumulation, checkpointing, memory optimization, and distributed training strategies where appropriate.

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices.

Build model-training workflows covering event/token sequence preparation, tokenization, temporal and positional representations, self-supervised pretraining, and downstream fine-tuning when appropriate to the use case.

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data.

Deep Learning & Model Architecture

Data Platforms & ML Pipelines

Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications.
Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution.
Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference.
Use PySpark and distributed processing techniques where appropriate for large-scale workloads.
Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation.

Productionization & MLOps

Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows.
Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices.
Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation.
Improve operational reliability, scalability, observability, and maintainability of ML systems.
Collaborate with platform, cloud, and DevOps teams on deployment automation and production support.

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards.

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations.

Software Engineering & Technical Ownership

Develop clean, modular, documented, and maintainable code following engineering standards and design principles.
Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems.
Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support.
Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency.
Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities.

Required Skills & Qualifications

Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality.
Strong ML foundation and mathematical understanding
Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring.
Strong SQL skills and experience working with structured data and data pipelines.
Hands-on experience with Snowflake for querying and preparing data for ML workflows.
Hands-on experience with Databricks for data processing and/or ML pipeline development.
Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate.
Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting.
Ability to design reliable, scalable, and maintainable software components and pipelines.

Familiarity with GPU training concepts and distributed strategies such as Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), or equivalents is an advantage for large-model workloads.

Understanding of model optimization, inference efficiency, and integration with production services or applications.

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership.

Preferred Skills

PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data.
MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management.
Experience integrating Snowflake and Databricks within enterprise ML workflows.
Cloud experience with AWS, Microsoft Azure, or Google Cloud.
REST APIs, containers, CI/CD, automated testing, logging, and observability.
Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications.
Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage.

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth.
Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues.

Role Summary

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps.

Key Responsibilities

Machine Learning Engineering

Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable.
Translate model requirements into robust, reusable, and testable Python components.
Build model training, validation, scoring, and inference workflows that can be integrated into production applications.
Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency.
Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior.

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices.

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data.

Deep Learning & Model Architecture

Data Platforms & ML Pipelines

Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications.
Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution.
Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference.
Use PySpark and distributed processing techniques where appropriate for large-scale workloads.
Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation.

Productionization & MLOps

Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows.
Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices.
Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation.
Improve operational reliability, scalability, observability, and maintainability of ML systems.
Collaborate with platform, cloud, and DevOps teams on deployment automation and production support.

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards.

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations.

Software Engineering & Technical Ownership

Develop clean, modular, documented, and maintainable code following engineering standards and design principles.
Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems.
Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support.
Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency.
Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities.

Required Skills & Qualifications

Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality.
Strong ML foundation and mathematical understanding
Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring.
Strong SQL skills and experience working with structured data and data pipelines.
Hands-on experience with Snowflake for querying and preparing data for ML workflows.
Hands-on experience with Databricks for data processing and/or ML pipeline development.
Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate.
Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting.
Ability to design reliable, scalable, and maintainable software components and pipelines.

Understanding of model optimization, inference efficiency, and integration with production services or applications.

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership.

Preferred Skills

PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data.
MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management.
Experience integrating Snowflake and Databricks within enterprise ML workflows.
Cloud experience with AWS, Microsoft Azure, or Google Cloud.
REST APIs, containers, CI/CD, automated testing, logging, and observability.
Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications.
Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage.

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth.
Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues.

Data Science and Machine Learning Associate

at Raah Techservices

Posted by Noora J

Bengaluru (Bangalore)

3 - 10 yrs

₹3L - ₹30L / yr

Python

SQL

Data Science

Machine Learning (ML)

Google Cloud Platform (GCP)

+1 more

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

Key Responsibilities

Develop and implement machine learning and predictive analytics models.
Perform data analysis, statistical modeling, and optimization to solve business problems.
Build demand forecasting and predictive models using time-series and other advanced techniques.
Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
Develop and maintain scalable data pipelines for ML model development and deployment.
Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
Validate models for accuracy, robustness, bias, and production readiness.
Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

Required Skills

Python
SQL
Data Science & Machine Learning
Predictive Modeling
Statistical Analysis
Machine Learning Algorithms
Optimization Techniques
Google Cloud Platform (GCP)
BigQuery
Dataflow
Dataproc
Data Fusion
Cloud SQL
Airflow
PySpark
PostgreSQL
Terraform
Tekton
APIs
MLOps

Preferred Skill

Java

Good to Have

Demand Forecasting
Time-Series Analysis
Neural Networks
Ensemble Methods
Support Vector Machines (SVM)
Regression and Cluster Analysis
ML Model Testing
Bias Detection and Data Drift Monitoring
Production ML Deployment
QlikSense
Automotive or Supply Chain Analytics

Education

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

Key Responsibilities

Develop and implement machine learning and predictive analytics models.
Perform data analysis, statistical modeling, and optimization to solve business problems.
Build demand forecasting and predictive models using time-series and other advanced techniques.
Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
Develop and maintain scalable data pipelines for ML model development and deployment.
Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
Validate models for accuracy, robustness, bias, and production readiness.
Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

Required Skills

Python
SQL
Data Science & Machine Learning
Predictive Modeling
Statistical Analysis
Machine Learning Algorithms
Optimization Techniques
Google Cloud Platform (GCP)
BigQuery
Dataflow
Dataproc
Data Fusion
Cloud SQL
Airflow
PySpark
PostgreSQL
Terraform
Tekton
APIs
MLOps

Preferred Skill

Java

Good to Have

Demand Forecasting
Time-Series Analysis
Neural Networks
Ensemble Methods
Support Vector Machines (SVM)
Regression and Cluster Analysis
ML Model Testing
Bias Detection and Data Drift Monitoring
Production ML Deployment
QlikSense
Automotive or Supply Chain Analytics

Education

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

Founding ML Engineer

at The Persona Labs

Posted by Saif Khan

Bhilai, Raipur

0 - 4 yrs

₹7L - ₹13L / yr (ESOP available)

Python

PyTorch

TensorFlow

PostgreSQL

Retrieval Augmented Generation (RAG)

+11 more

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences.

THE OPPORTUNITY

We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do.

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work.

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

THE OPPORTUNITY

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do.

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work.

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

Data Science Engineer

at Talview

1 video

3 recruiters

Posted by Ashwini R

Bengaluru (Bangalore)

3 - 5 yrs

₹15L - ₹25L / yr

Python

Statistical Analysis

Machine Learning (ML)

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Talview builds AI-powered hiring and assessment solutions for enterprise organisations. Our products support candidate screening, interview scheduling and online exam proctoring, helping teams improve efficiency and make better-informed decisions.

Role Overview

We are looking for a Data Science Engineer with strong hands-on experience in Python, SQL, statistics and classical machine learning, along with experience working on GenAI and Large Language Models (LLMs).

You will work across data processing, statistical analysis, experimentation, model development, evaluation and production AI/ML workflows. You will collaborate with Engineering and Product teams to translate business problems into measurable data science solutions.

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

Develop complex SQL queries for data extraction, transformation, analysis and validation.
Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
Analyse production data and troubleshoot data, model and pipeline issues.
Integrate data from APIs, databases and other structured and unstructured sources.
Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
Strong hands-on Python programming skills.
Strong SQL skills for complex querying, data transformation and analysis.
Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
Practical experience developing and evaluating classical machine learning models.
Experience with LLMs, prompting, structured outputs and evaluation.
Experience defining evaluation metrics, benchmarks and error-analysis approaches.
Ability to work with APIs, databases and structured and unstructured datasets.
Strong analytical and problem-solving skills.
Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

Experience designing end-to-end ML or LLM workflows.
Experience with dataset creation, sampling, annotation and ground-truth validation.
Experience analysing production AI/ML systems and debugging model or pipeline issues.
Experience building reusable experimentation and evaluation frameworks.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Classical ML, Hypothesis Testing, Experimental Design, Model Evaluation, Exploratory Data Analysis, GenAI, Large Language Models, LLM Evaluation, Error Analysis

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Role Overview

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

Develop complex SQL queries for data extraction, transformation, analysis and validation.
Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
Analyse production data and troubleshoot data, model and pipeline issues.
Integrate data from APIs, databases and other structured and unstructured sources.
Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
Strong hands-on Python programming skills.
Strong SQL skills for complex querying, data transformation and analysis.
Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
Practical experience developing and evaluating classical machine learning models.
Experience with LLMs, prompting, structured outputs and evaluation.
Experience defining evaluation metrics, benchmarks and error-analysis approaches.
Ability to work with APIs, databases and structured and unstructured datasets.
Strong analytical and problem-solving skills.
Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

Experience designing end-to-end ML or LLM workflows.
Experience with dataset creation, sampling, annotation and ground-truth validation.
Experience analysing production AI/ML systems and debugging model or pipeline issues.
Experience building reusable experimentation and evaluation frameworks.

Key Skills

AI/ML Engineer

at Cybotrix Technologies Pvt Ltd

Posted by Auni Kumar

Ahmedabad

2 - 3 yrs

₹5L - ₹7L / yr

Machine Learning (ML)

Artificial Intelligence (AI)

Python

Generative AI

Large Language Models (LLM)

+1 more

Job Overview

We are looking for an experienced AI/ML Engineer with strong full-stack development skills to design, develop, and deploy AI-powered applications. The ideal candidate should be proficient in Python backend development, frontend technologies, and LLM integration, with the ability to build end-to-end AI-native products, custom dashboards, and lightweight applications.

This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.

Key Responsibilities

Design, develop, and deploy end-to-end AI-powered applications.
Build backend services and REST APIs using Python.
Develop responsive frontends using React, Next.js, or similar frameworks.
Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
Rapidly prototype and validate ideas using modern AI-assisted development workflows.
Optimize application performance, scalability, reliability, and AI usage costs.
Work with data processing libraries such as Pandas and NumPy.
Collaborate with product and business teams to translate requirements into functional AI solutions.
Take ownership of features from ideation and development through testing, deployment, and production support.

Required Skills & Qualifications

2+ years of experience in software engineering, AI engineering, or application development.
Strong proficiency in Python and backend development.
Hands-on experience with React, Next.js, or similar frontend technologies.
Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
Understanding of REST APIs, microservices, and system design principles.
Knowledge of prompt engineering, embeddings, and RAG architectures.
Experience with data processing using Pandas and NumPy.
Ability to independently build, test, and deploy full-stack applications.
Strong problem-solving, debugging, and analytical skills.
Ability to work in a fast-paced environment with a strong ownership mindset.

Good to Have

Experience with LangChain, LlamaIndex, or similar AI frameworks.
Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
Familiarity with cloud platforms, application deployment, and production monitoring.
Experience with AI-assisted coding tools and rapid prototyping workflows.

What We Are Looking For

A full-stack AI engineer who is comfortable working across both backend and frontend development.
Someone focused on building practical AI-powered products rather than only training machine learning models.
A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
A self-driven professional who can take ownership of a feature from concept to production.

Please note that this job is only for Ahmedabad location.

Job Overview

This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.

Key Responsibilities

Design, develop, and deploy end-to-end AI-powered applications.
Build backend services and REST APIs using Python.
Develop responsive frontends using React, Next.js, or similar frameworks.
Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
Rapidly prototype and validate ideas using modern AI-assisted development workflows.
Optimize application performance, scalability, reliability, and AI usage costs.
Work with data processing libraries such as Pandas and NumPy.
Collaborate with product and business teams to translate requirements into functional AI solutions.
Take ownership of features from ideation and development through testing, deployment, and production support.

Required Skills & Qualifications

2+ years of experience in software engineering, AI engineering, or application development.
Strong proficiency in Python and backend development.
Hands-on experience with React, Next.js, or similar frontend technologies.
Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
Understanding of REST APIs, microservices, and system design principles.
Knowledge of prompt engineering, embeddings, and RAG architectures.
Experience with data processing using Pandas and NumPy.
Ability to independently build, test, and deploy full-stack applications.
Strong problem-solving, debugging, and analytical skills.
Ability to work in a fast-paced environment with a strong ownership mindset.

Good to Have

Experience with LangChain, LlamaIndex, or similar AI frameworks.
Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
Familiarity with cloud platforms, application deployment, and production monitoring.
Experience with AI-assisted coding tools and rapid prototyping workflows.

What We Are Looking For

A full-stack AI engineer who is comfortable working across both backend and frontend development.
Someone focused on building practical AI-powered products rather than only training machine learning models.
A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
A self-driven professional who can take ownership of a feature from concept to production.

Please note that this job is only for Ahmedabad location.

AI/ML Developer

at Wissen Technology

4 recruiters

Posted by Meghana Shinde

Pune, Bengaluru (Bangalore)

4 - 10 yrs

Best in industry

Python

Artificial Intelligence (AI)

Machine Learning (ML)

Natural Language Processing (NLP)

Location - Bangalore / Pune

Exp - 4 to 10 years

Required

· 3–8 years of relevant experience in data science, AI engineering, or applied machine learning roles.

· Bachelor’s or master’s degree in computer science, Mathematics, Engineering, or a related quantitative field.

· System design: ability to design scalable, reliable, and maintainable systems, including service decomposition, APIs, caching, queues and event-driven architectures, and the trade-offs between latency, throughput, cost, and consistency.

· Strong foundation in data structures and algorithms, with the ability to analyse time and space complexity and choose efficient approaches for large-scale data processing.

· Experience building and operating data-intensive or data-driven applications, such as large-scale data pipelines, batch and streaming processing, or applications whose core features depend on high-volume data.

· Strong hands-on experience in Python.

· Solid foundation in machine learning, NLP, and statistical modeling, including data exploration and visualization.

· Hands-on experience or projects involving LLMs, LangChain, LangGraph, RAG, or agentic AI systems.

· Experience delivering AI/ML solutions into production environments.

· Understanding of text processing and unstructured data.

· Knowledge of software engineering best practices (TDD, refactoring, modular design, version control).

· Strong foundation in data structures and algorithms, with the ability to analyze time and space complexity and choose efficient approaches for large-scale data processing.

· Strong analytical, problem-solving, and communication skills.

· Self-motivated, proactive, and delivery-oriented mindset.

Location - Bangalore / Pune

Exp - 4 to 10 years

Required

· 3–8 years of relevant experience in data science, AI engineering, or applied machine learning roles.

· Bachelor’s or master’s degree in computer science, Mathematics, Engineering, or a related quantitative field.

· Strong foundation in data structures and algorithms, with the ability to analyse time and space complexity and choose efficient approaches for large-scale data processing.

· Strong hands-on experience in Python.

· Solid foundation in machine learning, NLP, and statistical modeling, including data exploration and visualization.

· Hands-on experience or projects involving LLMs, LangChain, LangGraph, RAG, or agentic AI systems.

· Experience delivering AI/ML solutions into production environments.

· Understanding of text processing and unstructured data.

· Knowledge of software engineering best practices (TDD, refactoring, modular design, version control).

· Strong foundation in data structures and algorithms, with the ability to analyze time and space complexity and choose efficient approaches for large-scale data processing.

· Strong analytical, problem-solving, and communication skills.

· Self-motivated, proactive, and delivery-oriented mindset.

Lead Data Scientist

at Yugenai

Posted by Palak Agrawal

Remote only

4 - 5 yrs

₹30L - ₹35L / yr

MLOps

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities:

Analyse complex datasets to identify patterns, issues, and opportunities.
Formulate and validate hypotheses through structured analysis and experimentation.
Build and improve machine learning and anomaly detection solutions.
Design end-to-end solutions considering data, modelling, engineering, and production constraints.
Perform root cause analysis across models, data, and systems.
Work with engineering teams on feature pipelines, model inference, and production deployment.
Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
Guide team members on analysis, modelling, and solution design.
Proactively identify initiatives and roadmap items that can add value to the project.

Requirements:

Strong foundation in statistics and machine learning.
Strong hands-on experience with Python and SQL.
Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
Strong data analysis, hypothesis-generation, and problem-solving skills.
Experience with standard supervised and unsupervised ML techniques.
Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
Experience building and deploying production ML solutions.
Working knowledge of data engineering and real-time / batch inference environments.
Strong communication and stakeholder-management skills.
Ability to lead technical work and guide cross-functional teams.

Good to Have:

Experience in fraud detection or risk modelling.
Experience with Graph Neural Networks.
Exposure to real-time systems, streaming features, and low-latency data stores.
Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

Responsibilities:

Analyse complex datasets to identify patterns, issues, and opportunities.
Formulate and validate hypotheses through structured analysis and experimentation.
Build and improve machine learning and anomaly detection solutions.
Design end-to-end solutions considering data, modelling, engineering, and production constraints.
Perform root cause analysis across models, data, and systems.
Work with engineering teams on feature pipelines, model inference, and production deployment.
Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
Guide team members on analysis, modelling, and solution design.
Proactively identify initiatives and roadmap items that can add value to the project.

Requirements:

Strong foundation in statistics and machine learning.
Strong hands-on experience with Python and SQL.
Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
Strong data analysis, hypothesis-generation, and problem-solving skills.
Experience with standard supervised and unsupervised ML techniques.
Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
Experience building and deploying production ML solutions.
Working knowledge of data engineering and real-time / batch inference environments.
Strong communication and stakeholder-management skills.
Ability to lead technical work and guide cross-functional teams.

Good to Have:

Experience in fraud detection or risk modelling.
Experience with Graph Neural Networks.
Exposure to real-time systems, streaming features, and low-latency data stores.
Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

Senior AI/ML Engineer

at Timble Technologies

1 recruiter

Posted by Shefali Gupta

Remote, Delhi, Gurugram, Noida, Ghaziabad, Faridabad, Bengaluru (Bangalore)

2 - 10 yrs

₹5L - ₹15L / yr

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

Docker

API

Flask

+4 more

Job Title: Senior AI/ML Engineer

Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

Experience: 2 TO 5 Years

About Us

Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.

Role Overview

We are looking for a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub-second latency and high reliability across our enterprise products.

Key Responsibilities

· Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).

· GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

· Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

· MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

· Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

· Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

· Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

· Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

· GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

· Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

· Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

· Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

· Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

· Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

· Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Job Title: Senior AI/ML Engineer

Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

Experience: 2 TO 5 Years

About Us

Role Overview

Key Responsibilities

· GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

· Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

· MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

· Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

· Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

· Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

· Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

· GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

· Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

· Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

· Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

· Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

· Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

· Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Applied AI/ML Engineer

at NonStop io Technologies Pvt Ltd

6 recruiters

Posted by Kalyani Wadnere

Pune

4 - 7 yrs

Best in industry

Data Structures

Artificial Intelligence (AI)

Machine Learning (ML)

Scikit-Learn

TensorFlow

+4 more

About NonStop io Technologies

NonStop io Technologies is a value-driven company with a strong focus on process-oriented software engineering. We specialize in Product Development and have a decade's worth of experience in building web and mobile applications across various domains. NonStop io Technologies follows core principles that guide its operations and believes in staying invested in a product's vision for the long term. We are a small but proud group of individuals who believe in the 'givers gain' philosophy and strive to provide value in order to seek value. We are committed to and specialize in building cutting-edge technology products and serving as trusted technology partners for startups and enterprises. We pride ourselves on fostering innovation, learning, and community engagement. Join us to work on impactful projects in a collaborative and vibrant environment.

Brief Description:

We're seeking an AI/ML Engineer to join our team. As AI/ML Engineer, you will be responsible for designing, developing, and implementing artificial intelligence (AI) and machine learning (ML) solutions to solve real-world business problems. You will work closely with engineering teams, including software engineers, domain experts, and product managers, to deploy and integrate Applied AI/ML solutions into the products that are being built at NonStop io. Your role will involve researching cutting-edge algorithms and data processing techniques, and implementing scalable solutions to drive innovation and improve the overall user experience.

Responsibilities

● Applied AI/ML engineering; Building engineering solutions on top of the AI/ML tooling available in the industry today. Eg: Engineering APIs around OpenAI

● AI/ML Model Development: Design, develop, and implement machine learning models and algorithms that address specific business challenges, such as natural language processing, computer vision, recommendation systems, anomaly detection, etc.

● Data Preprocessing and Feature Engineering: Cleanse, preprocess, and transform raw data into suitable formats for training and testing AI/ML models. Perform feature engineering to extract relevant features from the data

● Model Training and Evaluation: Train and validate AI/ML models using diverse datasets to achieve optimal performance. Employ appropriate evaluation metrics to assess model accuracy, precision, recall, and other relevant metrics

● Data Visualization: Create clear and insightful data visualizations to aid in understanding data patterns, model behaviour, and performance metrics

● Deployment and Integration: Collaborate with software engineers and DevOps teams to deploy AI/ML models into production environments and integrate them into various applications and systems

● Data Security and Privacy: Ensure compliance with data privacy regulations and implement security measures to protect sensitive information used in AI/ML processes

● Continuous Learning: Stay updated with the latest advancements in AI/ML research, tools, and technologies, and apply them to improve existing models and develop novel solutions

● Documentation: Maintain detailed documentation of the AI/ML development process, including code, models, algorithms, and methodologies for easy understanding and future reference.

Qualifications & Skills

● Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, or a related field. Advanced degrees or certifications in AI/ML are a plus

● Proven experience as an AI/ML Engineer, Data Scientist, or related role, ideally with a strong portfolio of AI/ML projects

● Proficiency in programming languages commonly used for AI/ML. Preferably Python

● Familiarity with popular AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, etc.

● Familiarity with popular AI/ML Models such as GPT3, GPT4, Llama2, BERT etc.

● Strong understanding of machine learning algorithms, statistics, and data structures

● Experience with data preprocessing, data wrangling, and feature engineering

● Knowledge of deep learning architectures, neural networks, and transfer learning

● Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud) for scalable AI/ML deployment

● Solid understanding of software engineering principles and best practices for writing maintainable and scalable code

● Excellent analytical and problem-solving skills, with the ability to think critically and propose innovative solutions

● Effective communication skills to collaborate with cross-functional teams and present complex technical concepts to non-technical stakeholders