AI/ML engineer

JD: AI/ML Python Engineer – RAG & LLM Systems

Location: Pune (On-Site)

Experience: 4+ years

AI/ML Python Engineer

Key Responsibilities

AI/ML Development & Optimization

• Build and integrate GenAI and classical ML models using

  LangChain, LlamaIndex, Scikit-learn, TensorFlow, PyTorch

  .
• Optimize models via

  hyperparameter tuning, cross-validation

  , and monitor performance using observability tools.
• Improve accuracy, latency, and reliability of LLM-based pipelines.

RAG, Embeddings & Vector Search

• Implement

  RAG workflows

  , prompt-engineered LLM interactions, and embeddings-based similarity search.
• Work with vector DBs such as

  Pinecone, Weaviate, Supabase, PGVector

  .

Multimodal Data Processing

• Preprocess and manage

  text, image, and video

  datasets.
• Build lightweight

  multimodal pipelines

  for feature extraction and inference.

Backend & API Engineering

• Build scalable backend services using

  FastAPI, Django, Flask

  .
• Design clean

  RESTful APIs

  and integrate AI APIs, external SDKs, and authentication workflows.

Database & Data Workflow Management

• Work with

  PostgreSQL, MySQL, MongoDB

  and integrate vector search layers.
• Support ETL workflows and backend data pipelines.

Task Automation & Scheduling

• Implement background tasks using

  Celery

  , Redis queues, and cron-based scheduled jobs.

Containerization & Deployment

• Package and deploy applications using

  Docker

  across dev, staging, and production environments.

End-to-End Development

• Contribute across the SDLC—from requirement analysis, design, development, testing, deployment, and optimization.

Required Skills

• Strong Python expertise
• Hands-on with

  Llama, LangChain, LlamaIndex

• Experience with

  Vector Databases

  (Pinecone, Weaviate, PGVector, etc.)
• Machine Learning & Deep Learning (Sklearn, TF, PyTorch)
• REST API development (FastAPI/Django/Flask)
• Docker, Celery, SQL/NoSQL Databases
• Knowledge of RAG, embeddings, prompt engineering
• Experience working with multimodal datasets

Nice to Have

• Experience with MLOps tools (MLflow, Weights & Biases)
• Cloud: AWS / GCP / Azure
• CI/CD pipelines
• Experience fine-tuning LLMs or building custom embeddings models