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Niche, frontier agent skills reduce applicant competition relative to general ML roles.
Highly specialized agentic and RAG expertise makes skills less transferable across unrelated industries.
Many mandatory, specialized technical requirements and production agent experience increase filtering rigor.
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Design and implement agentic systems for multi-step reasoning, planning, tool use, and orchestration in B2B operational workflows.
Develop and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines and manage memory and context for conversational AI systems.
Build production-quality agent systems with strong observability, safety controls, integration with enterprise tools, and evaluation of multi-step task success and failure modes.
Demonstrated experience building and shipping production agentic systems with modern orchestration frameworks such as LangGraph or LangChain.
Strong Python engineering skills with experience in testing, CI/CD, version control, and API integration for LLM-based systems in production.
Hands-on experience with frontier model platforms (e.g., Anthropic, Google, OpenAI) or open-weight/self-hosted models (e.g., Llama via vLLM).
Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
Experienced in engineering long-horizon reliable agent systems handling failure recovery, self-corrections, and human-in-the-loop checkpoints.
Skilled in designing and optimizing RAG systems with deep understanding of memory, context management, and LLM behavior (strengths, limitations, hallucinations).
Proficient in implementing observability, tracing, safety guardrails, and evaluation techniques focused on multi-step task trajectories and agent reliability.