Staff Engineer- Applied AI

• Build and own the recommendation engine - Design and implement the ML pipeline that recommends solution type (technology, layout, configuration) from customer inputs; own the feature store, training pipeline, model registry and serving layer.
• Build the constraint-solver intelligence - Implement the AI layer on top of the constraint solver: learned priors for solver warm-starts, anomaly detection for unusual input combinations, and automated sensitivity analysis.
• Build LLM-based data ingestion- Design the production pipeline that takes unstructured customer documents (PDFs, spreadsheets, emails) and extracts structured Sizer inputs using LLMs; handle uncertainty, partial extractions and human-in-the-loop review.
• Build the formulator engine and vector DB- Implement semantic search over historical sizing solutions and L1 data sheets; design the embedding pipeline, vector database and retrieval-augmented generation (RAG) layer.
• Define AI chapter patterns- Set the shared patterns that all squads adopt: LLM API integration (streaming, function calling, structured output), prompt engineering standards, evaluation harnesses and A/B experiment frameworks for AI features.
• Build the AI evaluation framework- Implement offline and online evaluation for every AI feature: golden-set benchmarks, production-shadow evaluation, A/B experiment readout tooling and regression detection.
• Own the AI API contracts- Design the internal API contracts between the AI squad and consuming squads (Sizer, Layout, GCM); version them, document them and enforce them via contract tests.
• Mentor and grow AI capability- Pair with and review engineers across squads who are implementing AI features; run the Applied AI chapter meetings; grow the team's collective AI engineering capability.

MINIMUM (REQUIRED) QUALIFICATIONS

• 10–15 years professional engineering; at least 4 years in applied ML / LLM productionisation.
• Deep experience with LLM APIs (OpenAI, Anthropic, or open-source): function calling, structured output, streaming, embeddings.
• Production RAG system experience: chunking strategies, embedding models, vector databases (Pinecone, Weaviate, Qdrant, pgvector), retrieval evaluation.
• Experience building and deploying recommendation systems or ranking models.
• Strong software engineering foundations: API design, observability, testing, CI/CD.
• Track record of cross-team technical influence: setting patterns others adopt.
  • Experience with ML experiment tracking and model-lifecycle management.