SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Oyster is a global employment platform enabling companies to hire, pay, and manage talent anywhere in the world. The company is fully distributed across 60+ countries and has been recognized as a top WorkTech company by TIME and Statista, a Newsweek Great Startup Workplace, and a G2 Leader in multiple categories. Oyster is also B Corp-certified.
As a Senior AI Platform Engineer, you will build the foundational AI infrastructure layer that enables teams across Oyster to develop, deploy, and operate production-grade AI applications at scale. You'll work hands-on designing and building reusable platform capabilities for LLM applications, AI agents, RAG systems, tool calling, and AI workflows. Your work will sit beneath application teams, providing shared infrastructure, services, integrations, and developer tooling that abstract away foundational complexity.
Key responsibilities include: designing reusable platform capabilities for LLM applications and AI agents; building data and knowledge pipelines for ingestion, embeddings, retrieval, and vector search; developing secure integrations between AI applications and enterprise systems using APIs and Model Context Protocol; creating reusable frameworks and libraries so engineering teams can build AI applications efficiently; establishing patterns for AI deployment, observability, evaluation, and lifecycle management; taking AI capabilities from prototype to production; ensuring security, privacy, and data governance compliance; and evaluating emerging AI models and infrastructure technologies.
You'll partner closely with Engineering, Product, and IT teams to establish platform primitives and technical patterns that allow Oyster to build and scale AI securely and effectively.
Required qualifications: 5+ years in data engineering, platform engineering, backend engineering, or related discipline with production systems experience; strong Python and SQL; hands-on experience building and deploying production LLM/generative AI applications; experience with RAG, embeddings, vector search, tool calling, AI agents, or enterprise knowledge systems; strong data engineering fundamentals including pipelines, data modeling, and data quality; experience with Snowflake, Databricks, dbt, Airflow, or similar tools; experience building shared AI infrastructure and platforms; demonstrated ability to take AI systems from experimentation to reliable production; solid understanding of security, authentication, privacy, and data governance; strong communication skills.
Bonus experience includes: AWS Bedrock or managed foundation-model platforms; Model Context Protocol (MCP); LLM evaluation and observability; LangChain/LangGraph; vector databases; AI security and governance; internal developer platforms and SDKs.
Role requires reliable home internet and fluent English. Position is fully remote but requires timezone availability within UTC−6 to UTC+3.
