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AI Engineer at Bobsled. . Location: Remote (US/Europe). About Bobsled. . Bobsled is building AI-powered analytics experiences that turn natural language into accurate, production-grade insights. We’re looking for a hands-on AI Engineer to drive text-to-SQL accuracy and the systems that make our LLM-based application reliable in production.. What You’ll Do. . . Own the text-to-SQL accuracy problem end-to-end: design evals, iterate prompts, and improve retrieval/routing. . Build and operate the experimentation and evaluation loop (automatic evals, regression suites, dataset curation). . Design pragmatic LLM application architectures (RAG, agent routing, tool-use orchestration) optimized for accuracy and latency. . Ship production-grade code and support deployments; instrument, monitor, and troubleshoot model behavior in real customer environments. . Partner closely with engineering and customers to improve semantic models, SQL generation, and data alignment. . Create feedback loops from users to systematically capture issues and convert them into measurable improvements. . Contribute to automation of environment provisioning and dev workflows to enable fast iteration. . . What We’re Looking For. . . 2+ years in ML/AI or data-focused engineering or data science roles building production systems data or AI systems. . Demonstrated experience tuning LLM applications: prompt engineering, evals, retrieval, agent design, or similar. . Strong hands-on coding in Python or TypeScript (TypeScript familiarity a plus; willingness to work across the stack required). . ML engineering mindset beyond notebooks: testing, CI, observability, performance, and deployment in production. . Comfort with SQL and complex data modeling; familiarity with data warehouses and pipelines. . Pragmatic, product-oriented approach—optimize for impact over novelty; complement existing systems rather than rebuild from scratch. . Ability to design experiments, quantify improvements, and communicate trade-offs clearly. . . Nice to Have. . . Experience with text-to-SQL systems, semantic layers, or BI/analytics workflows. . Exposure to RAG frameworks, knowledge graphs, vector stores, and evaluation tooling. . Prior work in analytics engineering or data engineering environments. . . Success Looks Like. . . Measurable improvements in text-to-SQL accuracy across target datasets and partners. . Reliable eval pipeline and regression suite running in CI to catch degradations. . Clear architecture and documentation for context/agent systems that others can contribute to. . Short feedback cycles with partners leading to fast, meaningful product wins. . . Compensation. . . Competitive salary and meaningful equity. . Comprehensive benefits. . .  . -Remote