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Common ML/GenAI title, broad skillset and metro Pune location increase candidate competition.
Specialized ML/GenAI and MLOps skills limit cross-industry fit.
Many mandatory technical areas (LLMs, MLOps, vector DBs, cloud) increase filtering rigor.
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Build and deploy end-to-end LLM-based Generative AI applications using multiple LLM platforms (OpenAI, Azure, Gemini, Claude, LLaMA, Mistral).
Design and implement agentic and multi-agent systems (MCP, A2A) with RAG pipelines and document intelligence solutions.
Productionize, deploy, monitor, and maintain AI backend services and UIs on cloud platforms ensuring performance, security, scalability, and Responsible AI compliance.
Strong Python programming skills including FastAPI and Flask frameworks.
Hands-on experience with large language models (LLMs), embeddings, prompt engineering, and agentic systems using MCP/A2A.
Experience with RAG architectures and vector databases such as FAISS, Pinecone, Weaviate, Chroma, or Milvus.
Work Experience Required: Not explicitly mentioned in the JD.
Proficient in modern GenAI frameworks (e.g., LangChain, LangGraph, LlamaIndex) and backend AI service development.
Familiarity with deployment and MLOps on cloud platforms (Azure, AWS, GCP) including containerization (Docker, Kubernetes) and CI/CD processes.
Experienced in integrating AI APIs with frontend technologies (React preferred) and skilled in designing scalable, secure, and Responsible AI-compliant solutions.