Principal Generative AI Engineer

CONFIDENTIAL Scovai, a pioneering force in the Talent Acquisition & Recruiting sector, is transforming how organizations build and manage their workforce through an intelligent, auditable AI platform that spans the entire employee lifecycle. As part of a confidential search, Scovai is seeking a Principal Generative AI Engineer to serve as the technical cornerstone of a rapidly growing AI engineering team. In this role, you will architect and deliver production-grade generative AI systems — encompassing LLM fine-tuning, retrieval-augmented generation (RAG) pipelines, multi-agent frameworks, and responsible AI tooling — that directly power Scovai's mission of combining algorithmic precision with human judgment.

You will set the engineering standards for model evaluation, system scalability, and AI safety, while collaborating cross-functionally to translate complex business requirements into robust, maintainable AI infrastructure. Your deep familiarity with foundation model ecosystems — including OpenAI, Anthropic, and leading open-source models — will be instrumental in shaping the technical direction of the platform and mentoring the engineers around you.

This is a rare opportunity to operate at the intersection of cutting-edge generative AI research and real-world product impact, working on systems that redefine how companies hire, develop, and augment their talent. If you are passionate about responsible AI, thrive in a high-ownership environment, and want your work to meaningfully shape the future of work, this role offers the scope and influence to do exactly that. Hybrid work model based in San Francisco.

Deep expertise in model optimization and inference engineering (quantization, pruning, distillation, tensor/model parallelism) for cost‑efficient, low‑latency LLM serving

Proven MLOps and production deployment experience (Kubernetes, Docker, Terraform, CI/CD/GitOps, model versioning and rollout strategies)

Hands‑on experience with vector databases and retrieval infrastructure (FAISS, Pinecone, Weaviate, Milvus) and building scalable semantic search pipelines

Strong background in data privacy, security, and regulatory compliance for AI systems (GDPR, HIPAA, encryption, secure enclaves, access controls, auditability)

Experience designing human‑in‑the‑loop workflows, active learning/annotation pipelines, and UX patterns for explainability, feedback capture, and model correction