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Metro location plus broad LLM/agent skillset yields moderate competition despite niche agent focus.
Core ML/LLM skills transfer across industries, but agent-specific tooling increases domain sensitivity.
Explicit 0–3 years plus mandatory hands-on LLM, Claude Code, and agent-framework experience raises filtering strictness.
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Design, build, and optimize intelligent AI agents using modern LLMs and agentic AI frameworks with capabilities like retrieval-augmented generation, tool calling, human-in-the-loop workflows, and multi-step reasoning.
Develop and maintain agent memory, context management, conversation state, and integrate AI agents with external APIs, enterprise tools, databases, and internal services.
Evaluate and benchmark agent performance using metrics and collaborate cross-functionally to translate business requirements into AI-powered production-grade solutions.
0-3 years of experience in AI Engineering, Software Engineering, or related fields.
Hands-on experience building AI agents using LLMs and agentic AI frameworks (e.g., LangChain, LangGraph).
Proficiency in at least one programming language, preferably Python or JavaScript/TypeScript.
Experience with prompt engineering, agent context tuning, memory management for AI agents, and using Claude Code for AI-assisted software development.
Strong software engineering fundamentals with ability to build reliable, production-ready AI systems beyond experimental prototypes.
Experienced in architecting agent-based systems involving multi-agent orchestration, human-in-the-loop workflows, and external API integrations.
Comfortable in rapid prototyping and iterative development with a focus on measurable AI agent performance and engineering impact.