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Specialized agentic LLM skills reduce applicants, but popular LLM field and mid-tier employer keep competition moderate.
Highly domain-specific LLM/agent engineering skills and production fine-tuning limit cross-industry transferability.
Explicit minimum experience plus mandatory LLM, agent, fine-tuning, and cloud/MLOps skills make filters strict.
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Design, build, and orchestrate autonomous AI agent systems integrating large language models, tool-use frameworks, and enterprise data pipelines.
Develop reliable, production-grade agentic AI solutions including multi-agent workflows with safety guardrails, memory, and fallback mechanisms.
Implement evaluation, security, and optimization frameworks covering agent performance, latency, cost, and scalability in production environments.
Minimum 2 years AI engineering experience, including at least 1 year with LLM/agent systems in production.
Proficiency in Python and experience with agent frameworks such as LangChain/LangGraph, AutoGen, CrewAI, or equivalent.
Hands-on experience in prompt engineering, agent architectures (ReAct, plan-and-execute, reflection loops), tool-use integrations (REST APIs, vector DBs, SQL executors).
Experience with cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), and MLOps/AIOps tools (MLflow, Weights & Biases).
Experience designing and implementing complex agent reasoning loops, memory and caching layers for scalable, production-grade AI agents.
Proven ability working with advanced LLM fine-tuning techniques (LoRA, PEFT, QLoRA, RLHF) and secure tool orchestration with Model Context Protocol (MCP) systems.
Demonstrated expertise in building evaluation frameworks and embedding security/governance safeguards against prompt injection and hallucinations.