Python Developer (AI/ML)

Python Developer with AI/ML Expertise

Designation:

Location: Turbhe, Navi Mumbai

CTC: 6-12 LPA

Years of Experience: 2-5 years

IMMEDIATE JOINERS REQUIRED

Your Responsibilities:

Backend Development & Architecture

• Design, develop, and maintain robust Python applications using Django or FastAPI and other frameworks.
• Build & consume RESTful and GraphQL APIs using industry best practices.
• Design and optimize relational databases (PostgreSQL, MySQL) with proper indexing and query optimization.
• Implement caching strategies using Redis or Memcached for improved performance.
• Containerize microservices with Docker and collaborate on Kubernetes-based deployments.
• Implement background task queues using Celery with message brokers (RabbitMQ/Redis), including smart retry and alerting mechanisms.
• Set up WebSocket consumers via Django Channels & FastAPI for real-time updates.
• Configure infrastructure on AWS (EC2, S3, RDS, Lambda, SQS, CloudWatch, SageMaker), and manage monitoring.
• Implement authentication and authorization mechanisms (JWT, OAuth2) and follow OWASP security best practices.

AI/ML Engineering

• Develop, train, and optimize ML models using PyTorch, TensorFlow, and Keras.
• Build end-to-end LLM and RAG (Retrieval-Augmented Generation) pipelines using LangChain and LangGraph.
• Design and implement stateful, multi-agent AI workflows using LangGraph for complex decision-making systems.
• Work with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and implement prompt engineering strategies.
• Utilize Hugging Face Transformers for model fine-tuning and deployment.
• Integrate embedding models (OpenAI, Cohere, Sentence Transformers) for semantic search and retrieval.
• Collaborate with data scientists to convert prototypes into production-grade AI applications.
• Integrate NLP, Computer Vision, and Recommendation Systems into scalable products.
• Work with transformer-based architectures (BERT, GPT, LLaMA, Mistral, etc.) for real-world AI use cases.

Data & Vector Databases

• Implement pgvector for embedding storage and similarity search with efficient indexing strategies.
• Integrate vector databases (Pinecone, Weaviate, FAISS, Milvus) for retrieval pipelines.
• Handle data preprocessing, feature engineering, and ETL/ELT pipelines for ML workflows.

MLOps & Quality

• Version and track ML experiments using MLflow, Weights & Biases, or Neptune.
• Deploy ML models using model serving frameworks (TorchServe, BentoML, TensorFlow Serving).
• Implement model monitoring, drift detection, and retraining pipelines.
• Write automated tests using pytest or unittest with ≥80% coverage.
• Use code quality tools like Black, Flake8, and Mypy with type hints.
• Implement LLM evaluation frameworks (RAGAS, LangSmith) and performance benchmarking.

Integration & Collaboration

• Integrate external services using webhooks (Stripe, Razorpay, etc.).
• Create and maintain API documentation using Swagger/OpenAPI.
• Contribute to CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) and follow Git best practices.
• Participate in tech-talks, team learning sessions, and regular code reviews.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, AI/ML, or related field.
• 2–5 years of experience in Python development (Python 3.9+) with proven AI/ML project experience.
• Strong experience with Django and FastAPI frameworks (ORM, middleware, signals, async endpoints).
• Proficiency in SQL databases (PostgreSQL or MySQL) and ORMs (Django ORM, SQLAlchemy).
• Hands-on experience with Redis for caching and as a message broker.
• Strong grip on Python standard libraries and tools (NumPy, Pandas, etc.).
• Experience with API design and integration (REST/GraphQL).
• Hands-on with Celery and message brokers (RabbitMQ or Redis).
• Hands-on experience with LangChain and LangGraph for building LLM-powered workflows and RAG systems.
• Deep learning experience with PyTorch or TensorFlow.
• Experience with Hugging Face Transformers and model fine-tuning.
• Proficiency with LLM APIs (OpenAI, Anthropic, Azure OpenAI) and prompt engineering.
• Experience with embedding models and semantic search implementation.
• Experience deploying ML models and LLM apps into production systems.
• Proficiency in PostgreSQL with experience in pgvector for embedding storage and similarity search.
• Experience with model versioning and experiment tracking (MLflow, Weights & Biases, or Neptune).
• Understanding of ML model evaluation metrics and LLM evaluation frameworks.
• Hands-on with Docker and AWS (SageMaker preferred).
• Understanding of Kubernetes basics (deployments, services, pods).
• Skilled in Git workflows, automated testing (pytest), and CI/CD practices.
• Understanding of security principles for AI systems and secure coding practices.
• Excellent communication and analytical thinking.

Nice to Have

• Experience with async Python (asyncio, async/await).
• Experience with Flask or other Python frameworks.
• Advanced knowledge of LangGraph for building complex multi-agent systems and state machines.
• Experience with vector databases (Pinecone, Weaviate, FAISS, Milvus) for retrieval pipelines.
• Knowledge of advanced LLM fine-tuning techniques (LoRA, QLoRA, PEFT).
• Experience with model serving frameworks (TorchServe, BentoML, TensorFlow Serving).
• Familiarity with Airflow, Prefect, or Dagster for data and model pipelines.
• Experience with distributed training and GPU optimization.
• Knowledge of quantization and model compression techniques.
• Understanding of RLHF (Reinforcement Learning from Human Feedback).
• Experience with data versioning tools (DVC, Pachyderm).
• Knowledge of infrastructure as code (Terraform, CloudFormation).
• Experience with monitoring tools (ELK Stack, Datadog, Prometheus).
• Experience with A/B testing and experimentation frameworks for ML models.
• Experience with Streamlit or Gradio for ML demos and prototypes.
• Background in statistics, optimization, or applied mathematics.
• Basic frontend knowledge (JavaScript, React).
• Understanding of microservices design patterns and event-driven architecture.
• Knowledge of graph databases (Neo4j) for knowledge graphs.
• Contributions to AI/ML or LangChain or LangGraph open-source projects.

Why Join Us

• Competitive compensation and benefits.
• Work on cutting-edge LLM and AI/ML applications.
• A collaborative, innovation-driven work culture.
• Opportunities to grow into AI/ML leadership roles.
• Access to latest AI/ML tools and technologies.
• Learning budget for courses, conferences, and certifications.