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Tier-1 brand, mid-level AI role, metro context, and broad GenAI skillset increase applicant competition.
Role requires specialized GenAI and LLM tooling experience, making cross-industry transferability limited.
Explicit 5+ years requirement plus mandatory GenAI/MLOps skills and specific tooling increases shortlisting strictness.
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Design, develop, test, and deploy production or pilot Generative AI and agentic AI applications using LLM APIs and multi-agent frameworks.
Implement and optimize RAG pipelines with embedding creation, vector indexing, and advanced prompt engineering techniques to improve performance and reliability.
Establish testing and monitoring pipelines to evaluate model quality, safety, and cost efficiency, collaborating with cross-functional teams for solution alignment.
5+ years total IT experience with 2–3 years in AI/ML or Generative AI development, including hands-on work with LLM-based or agentic AI apps.
Proficiency in Python, familiarity with frameworks such as LangChain, LlamaIndex, CrewAI, and LangGraph.
Experience with RAG systems and vector databases like FAISS, Pinecone, Chroma, or Milvus.
Experience using prompt testing/evaluation tools (e.g., TruLens, LangSmith, PromptLayer, DeepEval).
Demonstrated ability to build complex multi-agent AI systems implementing inter-agent communication and task orchestration.
Strong background in applying advanced prompt engineering techniques including Few-Shot Learning, Chain-of-Thought, ReAct, and CART.
Experience working with cloud AI platforms (Azure, AWS, GCP, or OCI) and implementing MLOps/LLMOps for model deployment and monitoring.