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Niche LLM/agent specialization but mid-level metro role increases candidate competition.
Highly specialized agentic LLM expertise limits cross-industry transferability.
Multiple mandatory LLM, production, and MLOps requirements create stringent shortlisting filters.
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Design, build, and deploy autonomous AI agent systems capable of multi-step workflows including tool use, memory, fallback, and safety mechanisms.
Develop production-grade agent architectures integrating LLMs, tool-use frameworks, enterprise data pipelines, and cloud infrastructure with monitoring and CI/CD pipelines.
Implement evaluation, security, optimization, and feedback loops to ensure reliable, scalable, and secure AI agents for end-to-end business workflow automation.
Minimum 2 years of AI engineering experience, with at least 1 year focused on LLM/agent systems in production.
Proficiency in Python and hands-on experience with at least one agent framework (e.g., LangChain/LangGraph, AutoGen, CrewAI).
Experience with designing agent architectures (ReAct, plan-and-execute, reflection loops), prompt engineering, tool-use integrations (REST APIs, vector DBs, SQL executors).
Experience with cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), and MLOps/AIOps tooling.
Experienced in designing complex agentic AI systems including memory, caching, and context optimization for scalable production use.
Skilled in security and governance for AI agents including prompt injection defense and mitigation of hallucinations.
Familiarity with developing evaluation frameworks and integrating AI agents with enterprise APIs and workflows for end-to-end automation.