AI Researcher

About the Role

• We're looking for a Senior AI Research Engineer to drive applied research and production implementation of work ontology and agent intelligence systems at a U.S.-based AI-powered workforce platform. You'll operate at the intersection of research and engineering — evolving how real-world work is structured, modeled, and automated, and building the systems that generate agent recommendations, blueprints, and workflows.
• This is a role for someone who knows when to iterate quickly, when to dig deeper, and when to ship.

About You

• You have 5+ years of experience in ML or LLM engineering with production ownership and know how to take research from ideation to deployed systems.
• You're hands-on with LLMs, prompt engineering, RAG systems, and agent architectures — not just familiar with the concepts, but battle-tested in production.
• You translate ambiguous research problems into clear hypotheses, defined success criteria, and shippable outcomes.
• You work independently in fast-moving environments with high accountability and low hand-holding.
• You care about data quality, observability, and building systems that are reliable and measurable.

What You'll Be Doing

• Research and evolve work ontology and agent intelligence systems, including task and workflow modeling, agent capabilities, and agent blueprints grounded in real-world constraints.
• Define and implement evaluation frameworks, benchmarks, and success metrics for ontology quality, agent recommendations, and LLM outputs.
• Identify, collect, and curate data to support ontology evolution, agent modeling, and LLM systems.
• Design and build reliable, reproducible data and LLM pipelines for ingestion, enrichment, retrieval (RAG), and generation.
• Build observability into LLM and data pipelines — logging, tracing, evaluations, and quality monitoring.
• Continuously ship improvements and iterate based on metrics and real-world feedback.

What We're Looking For

• A rigorous research mindset balanced with a strong bias toward shipping — you know when results are good enough to move into implementation.
• Comfort operating with ambiguity and full ownership from research through production.
• Clear, direct communication and early escalation when blockers arise.
• A genuine interest in how work is structured, automated, and evolved in the AI era.

Technical Requirements

Must-Haves

• :
• 5+ years in ML or LLM engineering with end-to-end production ownership.
• Strong hands-on experience with LLMs, prompt engineering, RAG systems, and agent architectures.
• Solid Python skills and experience with ML/LLM frameworks (LangChain, PyTorch).
• Strong data engineering fundamentals: ETL, pipeline design, and data curation.
• Experience translating research or experimentation into reliable production systems.

Nice-to-Haves

• :
• Experience with work ontology modeling (roles, tasks, skills, workflows).
• Background in knowledge graphs or semantic modeling.
• Familiarity with AWS and event-driven architectures.
• Experience with FastAPI, Redis, MySQL, Terraform, or EKS.