Ai Ml Engineer

Role & responsibilities

Must Have:
Generative AI | LLM | Python | LangChain | LangGraph | RAG | FastAPI

Preferred:
MCP | AI Agents | Vector Databases | LLM Fine-tuning | Cloud | MLOps | Life Sciences

Must-have skills:

• Bachelors degree in Computer Science, Data Engineering, AI/ML, or a related field(Master’s preferred).
• 5+ years of experience in AI/ML, with significant hands-on experience in Generative AI and LLM-based applications.
• Strong hands-on experience with Generative AI / Large Language Models (LLMs), including model integration, evaluation, optimization, and deployment.
• Strong expertise in Python and experience building scalable AI applications and services.
• Hands-on experience with LangChain and LangGraph for developing LLM-powered applications, workflows, and AI agents.
• Strong experience building Retrieval-Augmented Generation (RAG) solutions, including document ingestion, chunking, embeddings, retrieval, reranking, context management, and response generation.
• Strong hands-on experience with FastAPI for developing and exposing AI/ML services and APIs.
• Experience working with LLM APIs and model providers such as OpenAI, Azure OpenAI, Hugging Face, Anthropic, Gemini, or equivalent.
• Good understanding of prompt engineering, embeddings, vector search, and LLM evaluation.
• Experience integrating GenAI solutions with SQL/NoSQL databases, vector databases, and enterprise data sources.
• Strong understanding of cloud platforms such as AWS, Azure, or GCP and their AI/ML services.
• Understanding of MLOps, model lifecycle management, deployment, monitoring, and productionization of AI solutions.
• Strong problem-solving, communication, and collaboration skills, with the ability to work with cross-functional teams.

Preferred Skills

• Model Context Protocol (MCP) experience, including building or integrating MCP servers/tools with LLM applications or AI agents.
• Experience developing AI Agents / Agentic AI workflows using LangGraph or similar frameworks.
• Experience with multi-agent systems, tool calling, function calling, and structured outputs.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, Milvus, or FAISS.
• Experience with LLM fine-tuning, LoRA/PEFT, or model optimization.
• Experience with Docker, Kubernetes, CI/CD, and cloud-native deployments.
• Strong understanding of AI security, data governance, responsible AI, and enterprise GenAI architecture.
• Life Sciences / Pharma / Healthcare domain expertise is highly desirable.
• Experience integrating GenAI solutions into production-level enterprise applications.