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Niche LLM/agent skillset at a lesser-known firm reduces applicant density despite metro location.
Specialized LLM, RAG and agent frameworks require ML-specific experience, somewhat limiting cross-industry transferability.
Extensive mandatory ML/LLM stack and tooling requirements plus explicit experience make filters stringent.
Design and implement Retrieval-Augmented Generation (RAG) pipelines and multi-agent AI systems integrating Large Language Models and agent orchestration frameworks.
Build, train, fine-tune machine learning models and develop scalable AI microservices with Python frameworks and vector databases.
Deploy, monitor, and collaborate on intelligent autonomous systems, contributing to AI infrastructure, reproducibility, and performance optimization.
Experience Required: 0-1 years (Fresher to junior level).
Proficiency in Python and ML frameworks (TensorFlow or PyTorch), and experience with RAG architectures.
Hands-on experience with microservice development (FastAPI/Flask) and multi-agent orchestration tools like LangChain or LangGraph.
Location: Mumbai or Pune based (onsite or hybrid not explicitly mentioned).
Familiar with end-to-end AI system development from data ingestion to deployment, especially involving LLMs and agentic reasoning.
Comfortable with building scalable, modular AI microservices integrated with vector databases and orchestration protocols.
Research-driven mindset towards novel AI architectures and prompt engineering with a technical focus on reproducibility and system performance.