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Tier-1 brand, mid-level GenAI role, metro locations, and broad skill requirements increase competition.
Core GenAI engineering skills transfer across industries, though domain experience is beneficial.
Explicit 5+ years, 1+ year GenAI requirement and many mandatory GenAI tech stacks make screening strict.
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Design, build, test, and deploy Generative AI applications using LLM APIs, prompt engineering, RAG pipelines, semantic search, vector databases, and agentic AI frameworks.
Develop enterprise solutions including chatbots, knowledge assistants, document intelligence, automation agents, and AI-led business process automation.
Build backend services and REST APIs using Python, FastAPI, Node.js integrating with databases, caching layers, and enterprise systems; support testing, evaluation, and observability of GenAI applications.
5+ years of professional experience with at least 1 year in GenAI/LLM ecosystems.
Bachelor's degree in Engineering, MCA, ME, MTech, MBA, or PGDM in regular full-time mode without extended course duration due to backlogs.
Hands-on skills in LLM APIs, prompt engineering, semantic search, vector databases, LangChain/LangGraph/CrewAI/LlamaIndex frameworks, backend API development using Python, FastAPI, Node.js.
Working knowledge of SQL/NoSQL databases, vector DBs, Redis, cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI), document processing, and basic knowledge of responsible AI and security practices.
Experienced developer comfortable with end-to-end GenAI application development and integration in production environments.
Demonstrated ability to work with a mix of cutting-edge GenAI frameworks and backend service architectures in agile, delivery-focused teams.
Candidates with knowledge of enterprise operations across sectors like banking, healthcare, insurance, retail, telecom, and familiarity with asynchronous processing, CICD, and multi-agent workflows are preferred.