Principal AI Engineer, Agentic Models and Data Platforms

Architect and implement agentic systems, including tool-using agents, workflow orchestrators, and multi-step reasoning pipelines.,Design and deliver Retrieval Augmented Generation (RAG) solutions, covering document ingestion, chunking, indexing, vector search, hybrid search, reranking, and grounding strategies.,Build evaluation harnesses and quality gates for AI systems, including offline test sets, golden datasets, regression suites, and metrics.,Implement observability for AI systems, including tracing, telemetry, drift detection, and runbooks.,Lead the build of batch and real-time data pipelines for analytics and AI use cases.,Design governed data products with clear contracts, documentation, lineage, and SLAs.,Establish high-quality ingestion, transformation, and serving patterns using lakehouse, warehouse, and streaming paradigms.,Partner with data stewards and domain teams to define data standards, quality controls, and metadata.,Design and build backend services and APIs exposing data products, agent capabilities, and AI workflows.,Apply rigorous engineering practices, including code quality, automated testing, CI/CD, performance engineering, and secure by default design.,Build scalable runtime patterns for AI systems, including caching, rate limiting, concurrency control, idempotency, and graceful degradation.,Contribute to reference architectures, reusable libraries, and platform components.

Bachelor's degree in computer science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience.,Master's degree in computer science or equivalent experience (Nice to have).

Bachelor's Degree