Staff AI Engineer

Staff AI Engineer, AI Platform & LLM InfrastructureCompensation: $250-$300k base + significant equityLocation: San Francisco, Hybrid, 2 days on-siteEmployment Type: Full-timeA fast-scaling, well-funded enterprise SaaS company transforming complex professional services workflows through applied AI.This company is modernizing a large, highly specialized industry where teams still rely heavily on manual, knowledge-intensive processes. Following a major recent funding round, the business is investing aggressively in AI as a core part of its product and engineering strategy not as an add-on, but as a fundamental rethink of how its platform is built.The engineering organization is now expanding its AI platform capabilities across evaluation, observability, data ingestion, orchestration, and production LLM infrastructure. This is an opportunity to join at a pivotal stage and help establish the technical foundations behind the next generation of AI-powered enterprise workflows.The RoleWe’re hiring a Staff AI Engineer to take a leading individual-contributor role within the company’s AI engineering organization.You’ll operate at the intersection of backend engineering, distributed systems, ML infrastructure, and applied AI building the platform that allows LLM-powered products to move reliably from prototype to production.The strongest fit will be an experienced systems-oriented engineer who has already shipped production AI systems and wants ownership over the difficult engineering problems behind them: evaluation, reliability, ingestion, observability, orchestration, retrieval, and quality.This is a high-autonomy Staff-level position with meaningful influence over technical direction, architecture, and engineering standards. For particularly strong candidates, there is also scope to help shape the company’s longer-term approach to AI evaluation and research.What You’ll DoArchitect and build core AI platform infrastructure supporting production LLM applications.Own systems across AI evaluation, observability, ingestion, orchestration, monitoring, and model quality.Design and scale production-grade RAG, embeddings, retrieval, and prompt infrastructure using complex domain-specific data.Develop robust evaluation frameworks for measuring model and system performance across real customer workflows.Build tooling around prompt and model versioning, experimentation, tracing, monitoring, and regression detection.Improve ingestion and data-processing pipelines that convert complex enterprise information into reliable inputs for AI systems.Take AI capabilities from early prototypes through to scalable, maintainable production systems.Establish engineering patterns and technical standards for how AI systems are developed, tested, deployed, and monitored.Partner closely with product, design, engineering, and domain experts to translate sophisticated professional workflows into practical AI copilots and automation.Provide technical leadership through architecture, code quality, design reviews, and mentorship while remaining deeply hands-on.Help determine where new models, agents, retrieval techniques, and AI infrastructure genuinely improve the product and where conventional software engineering remains the better solution.What You’ll BringStrong software engineering experience, ideally spanning backend systems, infrastructure, distributed systems, developer platforms, or ML/AI infrastructure.Demonstrable Staff-level technical ownership or evidence of operating at equivalent scope and complexity.Deep proficiency in Python and strong fundamental software engineering skills.Experience shipping LLM or applied AI systems into production, rather than solely building prototypes or research projects.Strong experience in one or more of:LLM evaluation and testingRAG and retrieval systemsAI/ML infrastructureAI agents and tool useModel observability and tracingData or ingestion pipelinesNLP and embeddingsDistributed backend systemsWhy Join?This is a rare opportunity to build foundational AI infrastructure at a company with the funding, executive commitment, customer demand, and technical ambition to deploy AI at meaningful scale.You’ll join while many of the most important architectural decisions are still being made, giving you genuine ownership over how production AI is evaluated, monitored, orchestrated, and scaled across the business.Rather than optimizing another thin layer around an external model API, you’ll tackle the harder problems that determine whether enterprise AI actually works: retrieval quality, evaluation, reliability, complex data ingestion, observability, workflow design, and production engineering.About People In AIPeople In AI is a specialist recruitment partner dedicated to connecting exceptional AI, machine learning, and data talent with some of the most ambitious technology companies in the market.We work closely with leading AI teams across startups, scale-ups, and established technology businesses, helping engineers and researchers find opportunities where they can have genuine technical impact.If you’re interested in building the infrastructure behind production-grade AI systems and want to operate with Staff-level ownership, we’d love to hear from you.