Python
PostgreSQL
Machine Learning (ML)Full-Stack Machine Learning Engineer
Role: Full-Time, Long-Term Required: Python Preferred: C++
OVERVIEW
We are seeking a versatile ML engineer to join as a core member of our technical team. This is a long-term position for someone who wants to build sophisticated production systems and grow with a small, focused team. You will work across the entire stack—from data ingestion and feature engineering through model training, validation, and deployment.
The ideal candidate combines strong software engineering fundamentals with deep ML expertise, particularly in time series forecasting and quantitative applications. You should be comfortable operating independently, making architectural decisions, and owning systems end-to-end.
CORE TECHNICAL REQUIREMENTS
Python (Required): Professional-level proficiency writing clean, production-grade code—not just notebooks. Deep understanding of NumPy, Pandas, and their performance characteristics. You know when to use vectorized operations, understand memory management for large datasets, and can profile and optimize bottlenecks. Experience with async programming and multiprocessing is valuable.
Machine Learning (Required): Hands-on experience building and deploying ML systems in production. This goes beyond training models—you understand the full lifecycle: data validation, feature engineering, model selection, hyperparameter optimization, validation strategies, monitoring, and maintenance.
Specific experience we value: gradient boosting frameworks (LightGBM, XGBoost, CatBoost), time series forecasting, probabilistic prediction and uncertainty quantification, feature selection and dimensionality reduction, cross-validation strategies for non-IID data, model calibration.
You should understand overfitting deeply—not just as a concept but as something you actively defend against through proper validation, regularization, and architectural choices.
Data Pipelines (Required): Design and implement robust pipelines handling real-world messiness: missing data, late arrivals, schema changes, upstream failures. You understand idempotency, exactly-once semantics, and backfill strategies. Experience with workflow orchestration (Airflow, Prefect, Dagster) expected. Comfortable with ETL/ELT patterns, incremental vs full recomputation, data quality monitoring, database design and query optimization (PostgreSQL preferred), time series data at scale.
C++ (Preferred): Experience valuable for performance-critical components. Writing efficient C++ and interfacing with Python (pybind11, Cython) is a significant advantage.
HIGHLY DESIRABLE: MULTI-AGENT ORCHESTRATION
We are building systems leveraging LLM-based automation. Experience with multi-agent frameworks highly desirable: LangChain, LangGraph, or similar agent frameworks; designing reliable AI pipelines with error handling and fallbacks; prompt engineering and output parsing; managing context and state across agent interactions. You do not need to be an expert, but genuine interest and hands-on experience will set you apart.
DOMAIN EXPERIENCE: FINANCIAL DATA AND CRYPTO
Preference for candidates with experience in quantitative finance, algorithmic trading, or fintech; cryptocurrency markets and their unique characteristics; financial time series data and forecasting systems; market microstructure, volatility, and regime dynamics. This helps you understand why reproducibility is non-negotiable, why validation must account for temporal structure, and why production reliability cannot be an afterthought.
ENGINEERING STANDARDS
Code Quality: Readable, maintainable code others can modify. Proper version control (meaningful commits, branches, code review). Testing where appropriate. Documentation: docstrings, READMEs, decision records.
Production Mindset: Think about failure modes before they happen. Build in observability: logging, metrics, alerting. Design for reproducibility—same inputs produce same outputs.
Systems Thinking: Consider component interactions, not just isolated behavior. Understand tradeoffs: speed vs accuracy, flexibility vs simplicity. Zoom between architecture and implementation.
WHAT WE ARE LOOKING FOR
Self-Direction: Given a problem and context, you break it down, identify the path forward, and execute. You ask questions when genuinely blocked, not when you could find the answer yourself.
Long-Term Orientation: You think in years, not months. You make decisions considering future maintainability.
Intellectual Honesty: You acknowledge uncertainty and distinguish between what you know versus guess. When something fails, you dig into why.
Communication: You explain complex concepts clearly and document your reasoning.
EDUCATION
University degree in a quantitative/technical field preferred: Computer Science, Mathematics, Statistics, Physics, Engineering. Equivalent demonstrated expertise through work also considered.
TO APPLY
Include: (1) CV/resume, (2) Brief description of a production ML system you built, (3) Links to relevant work if available, (4) Availability and timezone.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Machine Learning (ML)
Deep LearningJob Title: AI/ML Engineer – Voice (2–3 Years)
Location: Bengaluru (On-site)
Employment Type: Full-time
About Impacto Digifin Technologies
Impacto Digifin Technologies enables enterprises to adopt digital transformation through intelligent, AI-powered solutions. Our platforms reduce manual work, improve accuracy, automate complex workflows, and ensure compliance—empowering organizations to operate with speed, clarity, and confidence.
We combine automation where it’s fastest with human oversight where it matters most. This hybrid approach ensures trust, reliability, and measurable efficiency across fintech and enterprise operations.
Role Overview
We are looking for an AI Engineer Voice with strong applied experience in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems.
This role requires someone who can design, build, deploy, and optimize end-to-end voice AI pipelines, including speech-to-text, text-to-speech, real-time streaming voice interactions, voice-enabled AI applications, and voice-to-LLM integrations.
You will work across core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. The ideal candidate is an applied engineer with strong fundamentals, the ability to prototype quickly, and the maturity to contribute to R&D when needed.
This role is collaborative, cross-functional, and hands-on.
Key Responsibilities
Voice AI Engineering
- Build end-to-end voice AI systems, including STT, TTS, VAD, audio processing, and conversational voice pipelines.
- Implement real-time voice pipelines involving streaming interactions with LLMs and AI agents.
- Design and integrate voice calling workflows, bi-directional audio streaming, and voice-based user interactions.
- Develop voice-enabled applications, voice chat systems, and voice-to-AI integrations for enterprise workflows.
- Build and optimize audio preprocessing layers (noise reduction, segmentation, normalization)
- Implement voice understanding modules, speech intent extraction, and context tracking.
Machine Learning & Deep Learning
- Build, deploy, and optimize ML and DL models for prediction, classification, and automation use cases.
- Train and fine-tune neural networks for text, speech, and multimodal tasks.
- Build traditional ML systems where needed (statistical, rule-based, hybrid systems).
- Perform feature engineering, model evaluation, retraining, and continuous learning cycles.
NLP, LLMs & GenAI
- Implement NLP pipelines including tokenization, NER, intent, embeddings, and semantic classification.
- Work with LLM architectures for text + voice workflows
- Build GenAI-based workflows and integrate models into production systems.
- Implement RAG pipelines and agent-based systems for complex automation.
Fintech & Banking AI
- Work on AI-driven features related to banking, financial risk, compliance automation, fraud patterns, and customer intelligence.
- Understand fintech data structures and constraints while designing AI models.
Engineering, Deployment & Collaboration
- Deploy models on cloud or on-prem (AWS / Azure / GCP / internal infra).
- Build robust APIs and services for voice and ML-based functionalities.
- Collaborate with data engineers, backend developers, and business teams to deliver end-to-end AI solutions.
- Document systems and contribute to internal knowledge bases and R&D.
Security & Compliance
- Follow fundamental best practices for AI security, access control, and safe data handling.
- Awareness of financial compliance standards (plus, not mandatory).
- Follow internal guidelines on PII, audio data, and model privacy.
Primary Skills (Must-Have)
Core AI
- Machine Learning fundamentals
- Deep Learning architectures
- NLP pipelines and transformers
- LLM usage and integration
- GenAI development
- Voice AI (STT, TTS, VAD, real-time pipelines)
- Audio processing fundamentals
- Model building, tuning, and retraining
- RAG systems
- AI Agents (orchestration, multi-step reasoning)
Voice Engineering
- End-to-end voice application development
- Voice calling & telephony integration (framework-agnostic)
- Realtime STT ↔ LLM ↔ TTS interactive flows
- Voice chat system development
- Voice-to-AI model integration for automation
Fintech/Banking Awareness
- High-level understanding of fintech and banking AI use cases
- Data patterns in core banking analytics (advantageous)
Programming & Engineering
- Python (strong competency)
- Cloud deployment understanding (AWS/Azure/GCP)
- API development
- Data processing & pipeline creation
Secondary Skills (Good to Have)
- MLOps & CI/CD for ML systems
- Vector databases
- Prompt engineering
- Model monitoring & evaluation frameworks
- Microservices experience
- Basic UI integration understanding for voice/chat
- Research reading & benchmarking ability
Qualifications
- 2–3 years of practical experience in AI/ML/DL engineering.
- Bachelor’s/Master’s degree in CS, AI, Data Science, or related fields.
- Proven hands-on experience building ML/DL/voice pipelines.
- Experience in fintech or data-intensive domains preferred.
Soft Skills
- Clear communication and requirement understanding
- Curiosity and research mindset
- Self-driven problem solving
- Ability to collaborate cross-functionally
- Strong ownership and delivery discipline
- Ability to explain complex AI concepts simply
Machine Learning (ML)PythonAbout Role
We are looking for a hands-on Python Engineer with strong experience in backend development, AI-driven systems, and cloud infrastructure. The ideal candidate should be comfortable working across Python services, AI/ML pipelines, and cloud-native environments, and capable of building production-grade, scalable systems.
This role offers high ownership, exposure to real-world AI systems, and long-term growth, making it ideal for engineers who want to build meaningful products rather than just features
Key Responsibilities
- Design, develop, and maintain scalable backend services using Python
- Build APIs and services using FastAPI, Flask, or Django
- Ensure performance, reliability, and scalability of backend systems
- Integrate AI/ML models into production systems (model inference, automation)
- Build and maintain AI pipelines for data processing and inference
- Deploy and manage applications on AWS, with exposure to GCP and Azure
- Implement CI/CD pipelines, containerization, and cloud deployments
- Collaborate with product, frontend, and AI teams on end-to-end delivery
- Optimize cloud infrastructure for cost, performance, and reliability
- Collaborate with product, frontend, and AI teams on end-to-end delivery
- Follow best practices for security, monitoring, and logging
Required Qualifications
- 2–4 years of professional experience in Python development
- Strong understanding of backend frameworks: FastAPI, Flask, Django
- Hands-on experience integrating AI/ML systems into applications
- Solid experience with AWS (EC2, S3, Lambda, RDS, IAM)
- Exposure to Google Cloud Platform (GCP) and Microsoft Azure
- Experience with Docker and CI/CD workflows
- Understanding of scalable system design principles
- Strong problem-solving and debugging skills
- Ability to work collaboratively in a product-driven environment
Perks and Benefits
- Work in Nikhil Kamath funded startup
- ₹3 – ₹4.6 LPA with ESOPs linked to performance and tenure
- Opportunity to build long-term wealth through ESOP participation
- Work on production-scale AI systems used in real-world applications
- Hands-on experience with AWS, GCP, and Azure architectures
- Work with a team that values clean engineering, experimentation, and execution
- Exposure to modern backend frameworks, AI pipelines, and DevOps practices
- High autonomy, fast decision-making, and real ownership of features and systems
PythonMachine Learning (ML)Level of skills and experience:
5 years of hands-on experience in using Python, Spark,Sql.
Experienced in AWS Cloud usage and management.
Experience with Databricks (Lakehouse, ML, Unity Catalog, MLflow).
Experience using various ML models and frameworks such as XGBoost, Lightgbm, Torch.
Experience with orchestrators such as Airflow and Kubeflow.
Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Fundamental understanding of Parquet, Delta Lake and other data file formats.
Proficiency on an IaC tool such as Terraform, CDK or CloudFormation.
Strong written and verbal English communication skill and proficient in communication with non-technical stakeholderst
PythonMachine Learning (ML)- Experience building and managing large scale data/analytics systems.
- Have a strong grasp of CS fundamentals and excellent problem solving abilities. Have a good
understanding of software design principles and architectural best practices.
- Be passionate about writing code and have experience coding in multiple languages, including at least
one scripting language, preferably Python.
- Be able to argue convincingly why feature X of language Y rocks/sucks, or why a certain design decision
is right/wrong, and so on.
- Be a self-starter—someone who thrives in fast paced environments with minimal ‘management’.
- Have exposure and working knowledge in AI environment with Machine learning experience
- Have experience working with multiple storage and indexing technologies such as MySQL, Redis,
MongoDB, Cassandra, Elastic.
- Good knowledge (including internals) of messaging systems such as Kafka and RabbitMQ.
- Use the command line like a pro. Be proficient in Git and other essential software development tools.
- Working knowledge of large-scale computational models such as MapReduce and Spark is a bonus.
- Exposure to one or more centralized logging, monitoring, and instrumentation tools, such as Kibana,
Graylog, StatsD, Datadog etc
.NETPython
Django
Flask- Work on a chatbot framework/architecture using an open-source tool or library
- Implement Natural Language Processing (NLP) for chatbots
- Integration of chatbots with Management Dashboards and CRMs
- Resolve complex technical design issues by analyzing the logs, debugging code, and identifying technical issues/challenges/bugs in the process
- Deploy applications using CI/CD tools
- Designing and building highly scalable AI and ML solutions
- Ability to understand business requirements and translate them into technical requirements
- Open-minded, flexible, and willing to adapt to changing situations
- Ability to work independently as well as on a team and learn from colleagues
- High adaptability in a dynamic start-up environment.
- Experience with bot multi-lingual utilization (preferred)
- Experience with bot human escalation
- Ability to optimize applications for maximum speed and scalability
- Come up with new approaches and ideas to improve the current performance of Chatbots across multiple domains and build a highly personalized user experience.
QUALIFICATIONS : B. Tech/ B.E. /M. Tech or a related technical discipline from reputed universities
SKILLS REQUIRED :
- Minimum 3+ years- of experience in Chatbot Development using the Rasa open-source framework.
- Hands-on experience building and deploying chatbots.
- Experience in Conversational AI platforms for enterprises using ML and Deep Learning.
- Experience with both text to speech and vice versa transformation incorporation.
- Should have a good understanding of various Chatbot frameworks/platforms/libraries.
- Build and evolve/train the NLP platform from natural language text data being gathered from users on a daily basis.
- Code using primarily Python.
- Experience with bots for platforms like Facebook Messenger, Slack, Twitter, WhatsApp, etc.
- Knowledge of digital assistants such as Amazon Alexa, Google Assistant, etc.
- Experience in applying different NLP techniques to problems such as text. classification, text summarization, question & answering, information retrieval, knowledge extraction, and conversational bots design potentially with both traditional & Deep Learning
- Techniques - NLP Skills/Tools: NLP, HMM, MEMM, P/LSA, CRF, LDA, Semantic Hashing, Word2Vec, Seq2Seq, spaCy, Nltk, Gensim, Core NLP, NLU, NLG, etc.
- Should be familiar with these terms: Tokenization, N-Grams, Stemmers, lemmatization, Part of speech tagging, entity resolution, ontology, lexicology, phonetics, intents, entities, and context.
- Knowledge of SQL and NoSQL Databases such as MySQL, MongoDB, Cassandra, Redis, PostgreSQL
- Experience with working on public cloud services such as Digital Ocean, AWS, Azure, or GCP.
- Knowledge of Linux shell commands.
- Integration with Chat/Social software like Facebook Messenger, Twitter, SMS.
- Integration with Enterprise systems like Microsoft Dynamics CRM, Salesforce, Zendesk, Zoho, etc.
MUST HAVE :
- Strong foundation in the python programming language.
- Experience with various chatbot frameworks especially Rasa and Dialogflow.
- Strong understanding of other AI tools and applications like TensorFlow, Spacy, and Google Cloud ML is a BIG plus.
- Experience with RESTful services.
- Good understanding of HTTPS and Enterprise security.
Machine Learning (ML)Responsibilities:
• Develop computer vision systems for enterprises to be used by hundreds of our
customers
• Enhance existing Computer vision systems to achieve high performance
• Prototype new algorithms rapidly, iterating to achieve high levels of performance
• Package these prototypes as robust models written in production level code to be
integrated into the product
• Work closely with the ML engineers to explore and enhance new product features
leading to new areas of business
Requirements:
Strong understanding of linear algebra, optimisation, probability, statistics
• Experience in the data science methodology from exploratory data analysis, feature
engineering, model selection, deployment of the model at scale and model evaluation
• Background in machine learning with experience in large scale training and
convolutional neural networks
• Deep understanding of evaluation metrics for different computer vision tasks
• Knowledge of common architectures for various computer vision tasks like object
detection, recognition, and semantic segmentation
• Experience with model quantization is a plus
• Experience with Python Web Framework (Django/Flask/FastAPI), Machine Learning
frameworks like Tensorflow/Keras/Pytorch
Python
JavaMachine Learning (ML)
Data Science
ScalaJavaPythonMachine Learning (ML)
JavaMachine Learning (ML)Python
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon