Machine Learning Engineer

Role Overview

We are looking for an AI/ML Engineer with 2 - 6 years of industry experience to develop and deploy machine learning solutions for industrial and smart manufacturing use cases. The role involves working with real-world industrial data, developing predictive and analytical models, and translating business and operational requirements into scalable AI/ML solutions.

The ideal candidate should have strong fundamentals in machine learning, Python, data processing, model development, and deployment, along with an interest in applying AI to manufacturing, industrial automation, and operational optimization.

Key Responsibilities

• Design, develop, train, and evaluate machine learning models for industrial and manufacturing use cases.
• Analyze large and complex datasets to identify patterns, trends, anomalies, and opportunities for optimization.
• Perform data preprocessing, feature engineering, model selection, and performance evaluation.
• Develop solutions for use cases such as predictive maintenance, anomaly detection, quality prediction, process optimization, forecasting, and equipment monitoring.
• Work with structured and time-series data generated from industrial equipment, machines, sensors, and operational systems.
• Develop and optimize ML pipelines for data preparation, model training, validation, and deployment.
• Collaborate with domain experts, data engineers, software engineers, and business stakeholders to understand requirements and translate them into technical solutions.
• Deploy machine learning models into production environments and monitor model performance.
• Troubleshoot model and data-related issues and continuously improve model accuracy, reliability, and scalability.
• Develop reusable code, APIs, and components to integrate ML models with enterprise and industrial applications.
• Document models, methodologies, experiments, results, and technical implementations.
• Stay updated with emerging AI/ML techniques, industrial AI trends, and smart manufacturing technologies.

Required Technical Skills

• Strong programming experience in Python.
• Good understanding of Machine Learning algorithms and concepts.
• Experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy, and preferably TensorFlow or PyTorch.
• Strong understanding of data preprocessing, feature engineering, model training, validation, and evaluation.
• Experience working with time-series data is preferred.
• Good understanding of statistical concepts and data analysis.
• Experience with SQL and relational databases.
• Understanding of model deployment and productionization of ML solutions.
• Familiarity with Git and software development best practices.
• Good problem-solving and analytical skills.

Preferred Skills

• Experience in Industrial AI, Smart Manufacturing, Industry 4.0, or Industrial IoT (IIoT).
• Experience with predictive maintenance, anomaly detection, forecasting, or quality inspection.
• Exposure to sensor data, machine/equipment data, telemetry, or real-time industrial data.
• Knowledge of computer vision for manufacturing or quality inspection use cases.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Familiarity with Docker, Kubernetes, or CI/CD for ML deployment.
• Knowledge of MLOps concepts and tools.
• Exposure to Generative AI/LLMs is an added advantage. 

Key Responsibilities:

• Develop and deploy machine learning, deep learning, and NLP models for various business use cases.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment.
• Optimize model performance and ensure scalability in production environments.
• Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions.
• Conduct data analysis to identify trends and insights.
• Implement MLOps practices for versioning, monitoring, and automating ML workflows.
• Research and evaluate new AI/ML techniques, tools, and frameworks.
• Document system architecture, model design, and development processes.

Required Skills:

• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
• Hands-on experience in building and deploying, finetuning ML/DL models in production.
• Good understanding of machine learning algorithms, neural networks, NLP, and computer vision.
• Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
• Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow.
• Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus.
• Strong analytical skills and ability to work with large datasets.
• Excellent communication and problem-solving abilities.
• Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc.).
• Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage.

Educational Qualification:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, IT, from IIT/NIT colleges strongly preferred

About Us:

The QX Impact was launched with a mission to make A.I accessible and affordable and deliver AI Products/Solutions at scale for the enterprises by bringing the power of Data, AI, and Engineering to drive digital transformation. We believe without insights; businesses will continue to face challenges to better understand their customers and even lose them. Secondly, without insights businesses won't’ be able to deliver differentiated products/services; and finally, without insights, businesses can’t achieve a new level of “Operational Excellence” is crucial to remain competitive, meeting rising customer expectations, expanding markets, and digitalization.

Role Overview

We are seeking a Machine Learning Engineer to lead the end-to-end development of production-grade analytical applications. This is a high-impact role requiring a blend of deep statistical modeling and machine learning. You will be responsible transforming raw consolidated data into high-accuracy forecasts through advanced feature engineering, rigorous model selection, and statistical validation.

This role is for an engineer who thrives in the research-to-code transition, ensuring that every model is mathematically sound, resistant to overfitting, and optimized for high-dimensional manufacturing data.

Responsibilities:

• Feature Engineering & Discovery: Design and build complex feature sets for diverse problem types, including behavioural features for churn, sensor-based lags for maintenance, and seasonal encodings for demand forecasting.
• Model Selection & Optimization: Conduct systematic experimentation across diverse algorithms (e.g., XGBoost, LightGBM, Prophet, or Deep Learning) to identify the best-performing models.
• Model Training & Testing: Develop, train, tune, and test a variety of ML architectures including time-series, classification and regression.
• Statistical Validation & Evaluation: Define and track complex evaluation metrics tailored to manufacturing, such as MAPE, RMSE, etc., while performing deep-dive bias-variance analysis.
• EDA & Research: Perform exploratory data analysis on consolidated "Gold" layer data to uncover hidden drivers of business outcomes and identify correlations between external signals.
• Refinement & Performance Tuning: Address critical modeling challenges including bias-variance tradeoffs, class imbalance, and overfitting to ensure models generalize to real-world production data.

Skills & Requirements:

• 3+ Years of Experience: Proven track record of developing and delivering production-grade ML models across multiple domains (Sales, Finance, Manufacturing, or Supply Chain).
• Mastery of the Python Ecosystem: Expert-level skills in Pandas, NumPy, Scikit-learn, and SciPy.
• Advanced Algorithmic Knowledge: Deep expertise in supervised and unsupervised learning, specifically ensemble methods (Boosting/Bagging) and time-series frameworks.
• Statistical Foundations: Strong grasp of hypothesis testing, probability distributions, and the mathematical principles behind model evaluation and optimization.
• SQL Proficiency: Expert ability to manipulate data within consolidated database layers to create the "Silver" feature sets required for training.
• Education: Bachelor’s or Master’s degree in a quantitative field (e.g., Data Science, Statistics, Mathematics, or Computer Science).
• Cloud Awareness: Experience with Azure Machine Learning or similar cloud modelling environments.
• Engineering Familiarity: Basic understanding of Docker, MLflow, or FastAPI for handing models off to deployment teams.

Personal Attributes:

• Strong problem-solving skills with a passion for data architecture.
• Excellent communication skills with the ability to explain complex data concepts to non-technical stakeholders.
• Highly collaborative, capable of working with cross-functional teams.
• Ability to thrive in a fast-paced, agile environment while managing multiple priorities effectively.

Competencies:

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

ML DEVELOPER

Hyperworks Imaging is a cutting-edge technology company based out of Bengaluru, India since 2016. Our team uses the latest advances in deep learning and multi-modal machine learning techniques to solve diverse real world problems. We are rapidly growing, working with multiple companies around the world.

JOB OVERVIEW

We are seeking a talented and results-oriented ML Developer to join our growing team in India. In this role, you will be responsible for developing and implementing new advanced ML algorithms and AI agents for creating AI assistants of the future. 

The ideal candidate will work on a complete ML pipeline starting from extraction, transformation and analysis of data to developing novel ML algorithms. The candidate will implement latest research papers and closely work with various stakeholders to ensure data-driven decisions and integrate the solutions into a robust ML pipeline.

RESPONSIBILITIES:

• Create AI agents using Model Context Protocols (MCPs), Claude Code, DsPy etc.
• Develop custom evals for AI agents.
• Build and maintain ML pipelines
• Optimize and evaluate ML models to ensure accuracy and performance.
• Define system requirements and integrate ML algorithms into cloud based workflows.
• Write clean, well-documented, and maintainable code following best practices

REQUIREMENTS:

• 2-3+ years of experience in data science, machine learning, or a similar role.
• Demonstrated expertise with python, PyTorch, and TensorFlow.
• Graduated/Graduating with B.Tech/M.Tech/PhD degrees in Electrical Engg./Electronics Engg./Computer Science/Maths and Computing/Physics
• Has done coursework in Linear Algebra, Probability, Image Processing, Deep Learning and Machine Learning.
• Has demonstrated experience with Model Context Protocols (MCPs), DSPy, AI Agents, MLOps etc

WHO CAN APPLY:

Only those candidates will be considered who,

• have relevant skills and interests
• can commit full time
• Can show prior work and deployed projects
• can start immediately

Please note that we will reach out to ONLY those applicants who satisfy the criteria listed above.

SALARY DETAILS: Commensurate with experience.

JOINING DATE: Immediate

JOB TYPE: Full-time

This is a remote position.

About Leegality:

Leegality works with large Indian businesses to digitally transform critical compliance processes in a fast, easy and secure way.

We have multiple products across 2 categories:

Document Infrastructure:

Products that help businesses build paperless processes at scale:

1. Document Execution Workflow: A unified platform for businesses to digitally execute (eSign, eStamp, Template Pre-fill, Document Fraud Prevention etc.) agreements, forms and other documents in a compliant way. Currently in use by 2000+ Indian businesses from giants like HDFC and SBI Cards to high-growth disruptors like goDigit and Cars24.
2. Contract Management: An AI-powered platform for businesses to quickly review, negotiate and take action on contract
3. Signstation: A simple platform for businesses to digitally sign simple documents like invoices, policies and letters in a cost effective manner

Consent Infrastructure:

1. Consentin: An end-to-end DPDP and Privacy compliance platform for Indian businesses
2. Consentin Lens: A data discovery platform for businesses to identify the personal data they collect and store.

If you’re interested in building mission critical software that operates at population scale (75 million + Indians have signed at least one document through Leegality) then join Leegality.

Curious about our impact? Explore our customer success stories: leegality.com/case-studies

Our Culture

At Leegality, trust, ownership, transparency, and having fun while doing meaningful work are core to how we operate — not just values on paper. Our team rated us an incredible 97 eNPS for FY 2023–24 — the highest among 175+ startups surveyed.

We focus deeply on helping our people grow and stay motivated. Some of the perks you’ll enjoy:

• Flexible working hours
• Hybrid work setup
• Bi-annual performance appraisals
• A culture that rewards initiative, curiosity, and impact

If you're looking for a place where you can make a real difference while working with smart, driven, and genuinely nice people, welcome to Leegality.

Location: Hybrid

Job Brief:

• As a Machine Learning Engineer specializing in Computer Vision (CV) and Natural Language Processing (NLP), you will develop solutions to interesting technical problems, exploring exciting growth opportunities and having a real impact on our product, particularly focusing on document and content intelligence.
• To ensure success, you should demonstrate solid data science knowledge and experience in a related ML, CV, or NLP role. A first-class engineer will be someone whose expertise enhances our systems for document intelligence and content processing

Responsibilities:

• Designing machine learning systems, self-running artificial intelligence (AI) software, and specialized models for Computer Vision and Natural Language Processing applications.
• Transforming data science prototypes and applying appropriate deep learning algorithms and tools to text and image/document data.
• Solving complex CV and NLP problems with multi-layered data types, such as image/document classification, information extraction, semantic search, and object detection.
• Optimizing existing machine learning models, with a focus on high-performance model deployment for CV and NLP tasks.
• Developing ML algorithms (including large language models/LLMs and computer vision models) to analyze huge volumes of historical text, image, and document data to make predictions and automate workflows.
• Running tests, performing statistical analysis, and interpreting test results for CV/NLP model performance.
• Documenting machine learning processes, model architectures, and data pipelines.
• Keeping abreast of developments in machine learning, Computer Vision, and Natural Language Processing.

Requirements:

• 3+ years of relevant experience in Machine Learning Engineering, with a strong focus on Computer Vision and/or Natural Language Processing.
• Advanced proficiency with Python.
• Extensive knowledge of ML frameworks, libraries (e.g., PyTorch, Transformers), data structures, data modeling, and software architecture.
• Experience with building and maintaining scalable RESTful APIs (e.g., FastAPI).
• In-depth knowledge of mathematics, statistics, deep learning (CNNs, RNNs, Transformers), and algorithms.
• Superb analytical and problem-solving abilities, especially for unstructured data challenges.
• Great communication and collaboration skills.
• Excellent time management and organizational abilities.
• Experience with cloud platforms (e.g., AWS) for model deployment and MLOps.

Recruitment Process:

• Our hiring process combines AI-powered evaluations with structured interviews to ensure a fair and seamless experience.
• You will be contacted via email with the next steps upon being shortlisted.
• The process may include Assessments, AI-enabled interviews, and In-Person Interviews with our team.
• Final selection and CTC will be based on your overall performance and experience.

Apply directly through our career page: https://careers.leegality.com/jobs/Careers

For more information about us please visit our:

Our Company and Culture: https://bit.ly/3Iqm5SB

Our Website: www.leegality.com/

Our LinkedIn Page: www.linkedin.com/company/leegality/

Leegality's Privacy Notice: https://www.leegality.com/employee-privacy-notice

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.

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

Principal Software Engineer

Company Summary :

As the recognized global standard for project-based businesses, Deltek delivers software and information solutions to help organizations achieve their purpose. Our market leadership stems from the work of our diverse employees who are united by a passion for learning, growing and making a difference. At Deltek, we take immense pride in creating a balanced, values-driven environment, where every employee feels included and empowered to do their best work. Our employees put our core values into action daily, creating a one-of-a-kind culture that has been recognized globally. Thanks to our incredible team, Deltek has been named one of America's Best Midsize Employers by Forbes, a Best Place to Work by Glassdoor, a Top Workplace by The Washington Post and a Best Place to Work in Asia by World HRD Congress. www.deltek.com

Position Responsibilities :

About the Role 

We are seeking a highly motivated AI Solutions Engineer to join Deltek’s growing AI Center of Excellence team to design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate combines deep expertise in AI, machine learning, Generative AI, Large Language Models (LLMs), SLMs, software engineering, cloud computing, and MLOps/LLMOps to build scalable, production-grade AI applications. 

The AI Solutions Engineer will collaborate with AI data scientists, architects, and engineering teams to deliver innovative AI-driven solutions while ensuring security, scalability, governance, and operational excellence. This role reports to the Senior AI Solutions Architect. 

Key Responsibilities 

AI & Machine Learning Development 

• Design, build, train, evaluate, and deploy machine learning and deep learning models. 
• Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral. 
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks. 
• Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions. 
• Optimize model performance, scalability, latency, and cost. 

Software Engineering & Solution Development 

• Develop production-grade AI applications using Python and modern software engineering practices. 
• Build APIs, microservices, and AI-powered enterprise applications. 
• Integrate AI services with enterprise systems, business applications, and data platforms. 
• Apply coding standards, automated testing, CI/CD, and version control best practices. 

MLOps & AI Operations 

• Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management. 
• Automate model training, validation, testing, and deployment processes. 
• Monitor model performance, data drift, hallucinations, and operational metrics. 
• Support continuous improvement and reliability of AI platforms. 

Cloud & Platform Engineering 

• Develop AI solutions on Azure, AWS, or Google Cloud platforms. 
• Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies. 
• Build scalable architectures supporting enterprise AI workloads and real-time inference. 

AI Governance & Security 

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements. 
• Implement model governance, explainability, bias mitigation, and risk management practices. 
• Maintain standards for secure design, deployment, and operation of AI solutions. 

Required Qualifications 

Education 

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field. 

Experience 

• 5+ years of software engineering or machine learning development experience. 
• 2+ years of hands-on experience developing and deploying Agentic AI, Generative AI or AI/ML solutions in production environments. 

Technical Skills 

Programming & Engineering 

• Strong expertise in Python. 
• Experience with Java, ReactJS, JavaScript, or similar programming languages. 
• Solid understanding of algorithms, data structures, APIs, and software design principles. 

Artificial Intelligence & Machine Learning 

• Machine Learning and Deep Learning concepts and frameworks. 
• Model training, evaluation, optimization, and deployment. 

Generative AI 

• Large Language Models (LLMs) & SLMs 
• Prompt Engineering 
• Retrieval-Augmented Generation (RAG) 
• AI Agents and Agentic Workflows 
• Fine-tuning and model customization 
• Vector embeddings and semantic search 

Frameworks & Tools 

• PyTorch, TensorFlow, Scikit-learn 
• LangChain, LlamaIndex, Semantic Kernel, MCP, A2A and Transformers 
• FastAPI, Flask 

Data & Analytics 

• SQL and NoSQL databases 
• Data pipelines, ETL, and data modeling 
• Experience with AWS, Azure and Google 

MLOps & DevOps 

• MLflow, Kubeflow, Azure ML, SageMaker 
• Docker and Kubernetes 
• Git, GitHub, Azure DevOps, Jenkins 
• CI/CD automation and model monitoring 

Cloud Platforms 

• AWS (preferred) 
• AWS Bedrock or Azure OpenAI Service 
• AWS SageMaker 
• Google Vertex AI 

Preferred Qualifications 

• Experience designing enterprise-scale AI platforms and products.  
• Knowledge of multi-agent architectures and autonomous AI systems.  
• Experience with vector databases such as Pinecone, Snowflake Cortex, Pgvector, Weaviate, Chroma, or Azure AI Search.  
• Understanding of AI governance, compliance, and Responsible AI frameworks.  
• Relevant certifications in Azure AI, AWS Machine Learning, or Google Cloud AI.

Job Description:

We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the

AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine Learning & LLM Capability:

 End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.  Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

 Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

 Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

 Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

 Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.

 Project Ownership & Execution

 Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

 Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

 Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.

 Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

 System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

 Performance Under Pressure

 Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.

 High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

 Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.

 Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications:

 Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.

 Proven expertise in Python, system design, and scalable AI/ML architecture.

 Deep knowledge of NLP, Computer Vision, and Deep Learning models.

 Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With 23,000 customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career; you become part of a team of Egnyters who are doers, thinkers, and collaborators who embrace and live by our values:

Invested Relationships

Fiscal Prudence

Candid Conversations

ABOUT EGNYTE

Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere.

WHAT YOU’LL DO: 

• Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)
• Optimize models for inference via quantization, pruning, and knowledge distillation
• Deploy models to edge devices, mobile, and local servers with strict latency targets
• Build end-to-end MLOps pipelines from data ingestion to deployment
• Monitor model accuracy, latency, and hardware utilization in production
• Evaluate model quality using benchmarking frameworks and custom evaluation suites

YOUR QUALIFICATIONS:

• SLM Development & Fine-tuning: Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
• Model Optimization: Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
• Edge Deployment: Deploy models to edge devices, mobile, and local servers, etc.
• Pipeline Engineering: Build end-to-end MLOps pipelines — from data ingestion to deployment.
• Performance Monitoring: Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

• Deployment experience on edge or mobile environments
• Knowledge of ONNX export and cross-platform inference
• MLOps tooling — experiment tracking, model registries, CI/CD for ML

EQUAL EMPLOYMENT OPPORTUNITY

At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.

Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.