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Mid-level GenAI role in metro with a well-known IT services brand increases candidate competition.
LLM, LangChain and vector DB expertise is specialized but broadly applicable across industries.
Explicit 4–9 years plus mandatory LangChain, RAG, vector DB and backend skills make filters strict.
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Design and develop Generative AI applications using Large Language Models (LLMs) and AI workflows with LangChain and LangGraph.
Develop scalable backend services and APIs in Python, integrating LLM platforms such as OpenAI, Azure OpenAI, or Anthropic into enterprise solutions.
Implement and optimize Retrieval-Augmented Generation (RAG) solutions using vector databases and perform prompt engineering for enhanced model performance.
4 to 8 years of hands-on Python development experience.
Strong knowledge and experience with Generative AI, LLMs, LangChain, LangGraph, and prompt engineering.
Experience with RAG architectures and vector databases like Pinecone, ChromaDB, Weaviate, or FAISS.
Work location: Bengaluru or Hyderabad, India.
Experienced in building AI agents, multi-agent systems, and orchestrating intelligent workflows for enterprise use cases.
Familiar with backend development practices including REST APIs, microservices, and cloud platforms (Azure, AWS, or GCP).
Comfortable with CI/CD pipelines and version control systems like Git for scalable AI solution deployment.