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Remote role and mid-level experience increase applicant density, but specialist LLM/agent focus reduces it.
Requires deep LLM, agentic system, and knowledge-graph expertise, making skills hard to transfer across domains.
Explicit 5+ years, 3+ years LLM focus, and mandatory LLM tooling and RAG experience make screening highly selective.
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Develop and own the intelligence layer atop the knowledge graph to convert structured regulatory and operational data into deterministic, citation-backed answers and autonomous agentic workflows.
Design, build, and maintain advanced LLM-based systems focusing on retrieval-augmented generation, multi-step reasoning, tool use, and task orchestration ensuring high accuracy and hallucination control.
Collaborate closely with cross-functional teams to maintain transparent communication and align model grounding with rigorous standards expected in high-stakes or regulated domains.
Minimum 5 years hands-on experience building and shipping production ML/AI systems; at least 3 years focused on LLMs, NLP, or generative AI.
Minimum 3 years production Python experience using frameworks like PyTorch, LangChain, or Hugging Face Transformers.
Proven expertise with retrieval-augmented generation architectures, agentic system design, prompt engineering, hallucination control, and citation accuracy.
Experience working with knowledge graphs/ontologies (e.g., SPARQL/RDF), function-calling/tool-use models (open-source and proprietary), fine-tuning/adapting LLMs, and cloud infrastructure collaboration (AWS or Azure).
Deep specialist in large language models, agent design, and retrieval-based generation rather than generalist ML engineering.
Experienced in high-stakes or regulated environments where deterministic, defensible AI output is critical.
Able to communicate complex model behaviors and trade-offs clearly to technical and non-technical stakeholders including leadership and go-to-market teams.