ML / RAG / Inference Engineer

**Mission** Build the AI orchestration layer for Federis so customers can govern retrieval, prompts, model access, inference routing, evaluation, and observability across replaceable external AI and vector systems. **Key Responsibilities** • Implement RAG workflows, retrieval configuration, prompt/version governance, inference routing, evaluation hooks, and model usage telemetry. • Build provider adapters for vector databases, model serving systems, embedding services, and customer-managed inference endpoints. • Create quality, latency, cost, grounding, and safety evaluation pipelines that support enterprise release gates and customer reporting. • Partner with security and product teams on policy enforcement for data access, prompt execution, model selection, and audit capture. • Design scalable data and inference paths that work across SaaS, private cloud, and air-gapped customer environments. • Document model and retrieval behavior clearly enough for solution engineers, customers, and auditors to understand. Required Experience • 4+ years in ML engineering, search, data platforms, RAG systems, model serving, or AI product engineering. • Strong software engineering ability in Python, TypeScript, Go, Java, or similar production languages. • Practical experience with embeddings, vector search, ranking, chunking, retrieval evaluation, prompt/version management, and model APIs. • Understanding of latency, throughput, cost, reliability, privacy, and observability tradeoffs in AI systems. • Ability to build adapter-based integrations without binding the core product to a single AI vendor or database. **Useful Differentiators** • Experience with vLLM, Triton, Milvus, Qdrant, Weaviate, OpenSearch, Apache Solr, or customer-hosted LLM stacks. • Experience creating AI evaluation harnesses, guardrails, prompt governance, red-team tests, or model risk workflows. • Prior work in regulated enterprise AI, data governance, knowledge management, or secure document intelligence. **Success Scorecard** • AI integrations are replaceable and measurable across quality, latency, cost, safety, and audit dimensions. • RAG workflows produce reproducible evidence for what data was used, what model was called, and what policy applied. • Customer deployments can use their preferred inference and vector systems without core rewrites. • Evaluation results inform roadmap priorities and customer success plans.