Applied AI Research Engineer
Core
Design and build grounding systems connecting agent reasoning to verified enterprise data; optimize retrieval pipelines (RAG, hybrid search); create evaluation frameworks for agent quality.
Role type
Applied AI Research Engineer (Research/Systems boundary)
Builds
Grounding systems, retrieval pipelines, evaluation infrastructure, production ML systems
Domain
Enterprise Data Intelligence, AI Agents, Information Retrieval
Deliverable
production ML models | product features
Required skills
Information retrieval, NLP, knowledge representation, RAG, evaluation methodology, Python, vector databases, embedding models, LLM APIs
Preferred skills
Enterprise data systems (SQL engines, catalogs), text-to-SQL, published research in IR/NLP, CI/CD evaluation pipelines, JVM-based systems
Technologies
Trino, Apache Iceberg, Python, vector databases, LLM APIs
Responsibilities
Design grounding systems for agent reasoning; build and optimize retrieval pipelines; define data representation strategies; create automated benchmarks and human evaluation protocols; convert research findings into production systems; establish quality metrics and dashboards; build feedback loops for grounding improvements
Seniority
Mid-Senior, hands-on IC
