Senior Data Engineer

✨ One platform, a whole world of opportunity The best jobs have always clustered in a handful of the world's wealthiest cities. But talent is everywhere. Oyster set out to close that gap - building a global employment platform that lets companies hire, pay, and care for brilliant people anywhere. We're proof that a high-performing culture doesn't need an office. Distributed across 60+ countries since 2020, we've built something the industry keeps noticing: Ranked #10 of 250 on TIME and Statista's 2026 list of America's Top WorkTech Companies Named one of America's Greatest Startup Workplaces 2026 by Newsweek A G2 Spring 2026 Leader across Employer of Record, Global Employment, Multi-country Payroll, and HR Compliance The only B Corp-certified global employment platform ~ independently verified since 2023 Two of those rankings measured our business impact from the outside. One measured how our own people feel about working here. They landed in the same place ~ because at Oyster, culture and performance aren't separate conversations. They're the same one. And we're just getting started. If you want to do the best work of your career alongside people who care as much as you do, we'd love for you to apply. 👩‍💻 The Role Location: While this position is posted in a specific location, all of Oyster’s positions are fully remote and you can work from home. Forever. To create the best experience for our new hire, this role requires you to be based within UTC−6 to UTC+3. Oyster’s Data Engineering team is building the foundational AI platform layer that will enable teams across Oyster to develop, deploy, and operate secure, production-grade AI applications. As an AI Platform Engineer , you’ll build the shared infrastructure, services, integrations, and developer tooling that sit beneath these applications. You’ll work hands-on with LLMs, AI agents, RAG, tool calling, and AI workflows , leveraging Oyster’s existing AWS/Snowflake data platform to turn AI use cases from experimentation into reliable, production-ready systems. You’ll partner closely with Engineering, Product, and IT to establish the platform primitives, technical patterns, and guardrails that allow Oyster to build and scale AI securely and effectively. Key Responsibilities Design and build reusable platform capabilities for LLM applications, AI agents, RAG, tool calling, and AI workflows . Build data and knowledge pipelines supporting ingestion, embeddings, retrieval, vector search, metadata, and knowledge management. Develop secure integrations between AI applications and enterprise data and systems using APIs, MCP, tool calling, and similar patterns . Build reusable frameworks, libraries, services, and developer tooling so engineering teams can build AI applications without repeatedly solving the same foundational problems. Establish patterns and standards for AI deployment, observability, evaluation, and lifecycle management , including quality, accuracy, latency, security, reliability, and cost. Help take AI capabilities from prototype to production , building for reliability, scalability, and maintainability. Ensure that the AI platform's capabilities meet Oyster’s security, privacy, access control, and data governance requirements. Evaluate emerging AI models, frameworks, and infrastructure technologies and determine where they can create meaningful value for Oyster. Core Requirements 5+ years in data engineering, platform engineering, backend engineering, or a related discipline, with experience building and operating production systems. Strong Python and SQL . Hands-on experience building and deploying production applications or services using LLMs / generative AI . Experience with RAG, embeddings, vector search, tool/function calling, AI agents, or enterprise knowledge systems . Strong data engineering fundamentals, including pipelines, data modeling, data quality, and secure data access . Experience with Snowflake, Databricks, or comparable modern data platforms , plus tools such as dbt, Airflow, or similar. Experience building shared AI infrastructure, platforms, or reusable AI capabilities , rather than only integrating AI into individual applications. Experience taking AI systems from experimentation to reliable production . Solid understanding of security, authentication/authorization, privacy, and data governance . Strong communicator who can translate ambiguous AI opportunities into practical engineering solutions across teams. Bonus Points Experience with AWS Bedrock or other managed foundation-model platforms. Hands-on experience with MCP (Model Context Protocol) or similar approaches for connecting AI to enterprise tools and data. LLM evaluation, observability, monitoring, or AI quality experience. Experience with LangChain, LangGraph , or similar frameworks. Vector database or search technology experience. Experience implementing AI security, governance, or responsible AI practices. Experience building internal developer platforms, SD