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Niche GenAI agent skillset (LangChain, vector DBs) reduces applicant density despite Pune metro hiring.
Specialized agentic GenAI, LangChain, and vector DB experience limits cross-industry transferability.
Explicit 5–9 years plus mandatory GenAI stack, vector DBs, model fine-tuning and AWS increases filter rigidity.
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Design and develop production-grade multi-agent AI systems and orchestration frameworks using tools like LangChain and LangGraph.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines and implement advanced prompt engineering for model accuracy and safety.
Lead fine-tuning of foundational AI models with proprietary data ensuring scalable, secure, and compliant AI workflows.
5 to 9 years total professional experience.
At least 1 to 3 years of Generative AI development experience within software engineering or machine learning.
Advanced proficiency in Python programming and experience with GenAI frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or Hugging Face.
Hands-on expertise with cloud AI deployment on AWS and working knowledge of vector databases like Pinecone, ChromaDB, or pgvector.
Experienced in designing complex multi-agent AI ecosystems and scalable AI orchestration pipelines.
Proficient in end-to-end GenAI system development including model fine-tuning, prompt engineering, and explainability tools like Langfuse or LangSmith.
Familiar with software engineering best practices including microservices architecture, REST APIs, Git, and Agile methodologies, enabling leadership in AI product implementation.