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Senior AI Engineer
Not specifiedFull-timeAI Engineering
Build production-grade AI and knowledge infrastructure for enterprise software, combining information retrieval, RAG, code intelligence, data ingestion, and secure multi-tenant systems.
Responsibilities
- Architect hybrid retrieval and RAG pipelines using embeddings, semantic search, lexical indexing, reranking, metadata filtering, chunking, and grounding.
- Design entity resolution, relationship inference, and dynamic knowledge graphs for software entities, ownership, controls, vulnerabilities, and provenance.
- Build code intelligence tooling based on AST parsing, call graphs, reference resolution, and compiler or LSP primitives.
- Develop low-latency ingestion pipelines that normalize, enrich, and synchronize structured and unstructured enterprise data.
- Create evaluation frameworks for retrieval precision and recall, hallucination reduction, citation accuracy, system health, latency, and cost.
- Collaborate with Product, Platform, and Cyber Security teams to turn enterprise requirements into production knowledge infrastructure.
Requirements
- Strong experience building complex backend, data, or AI infrastructure at scale.
- Production experience with search systems, vector databases, hybrid dense and sparse retrieval, reranking, and LLM orchestration.
- Deep knowledge of information retrieval, indexing, query expansion, relevance scoring, BM25, and search evaluation.
- Hands-on experience with AST parsing, symbol resolution, control-flow or data-flow analysis, or LSP integrations.
- Experience processing structured and unstructured data from repositories, ticketing systems, event logs, APIs, and relational or non-relational stores.
- Understanding of tenant isolation, authorization controls, data-leakage prevention, and provenance tracking.
- Ability to work autonomously in a fast-paced environment while balancing architecture and implementation.