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Mid-level Bangalore role at a known analytics firm, but niche GenAI/multi-agent skills reduce broad applicant pool.
Core GenAI and LLM engineering skills transfer across industries, but enterprise integration and agentic workflows increase domain specificity.
Explicit 3–8 years requirement plus mandatory GenAI, LangChain, vector DBs, and deployment skills enforce strict filtering.
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Design and build multi-agent AI systems using LLMs for planning, reasoning, and autonomous task execution across enterprise systems.
Develop and deploy AI copilots and autonomous assistants integrating with APIs, databases, and enterprise workflows.
Optimize LLM performance and implement MLOps+LLMOps practices ensuring AI safety, governance, and scalable autonomous AI architectures.
Bachelor’s or master’s degree in computer science, AI, or related field.
3–8 years of AI/ML work experience with strong focus on Generative AI.
Proficiency in Python and hands-on experience with LLM frameworks (OpenAI, Hugging Face Transformers) and agent frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel).
Experience with vector databases (FAISS, Pinecone, Weaviate), cloud platforms (Azure OpenAI preferred, AWS/GCP acceptable), and containerization tools (Docker, Kubernetes).
Experience building multi-agent orchestration systems with role-based coordination and agent planning algorithms (ReAct, Plan-and-Execute, Tree-of-Thought).
Strong systems thinking and problem decomposition skills to design autonomous AI architectures balancing latency, cost, and accuracy.
Proven ability to translate complex business workflows into AI agent pipelines and deploy production-ready agentic AI solutions in enterprise environments.