Fastmarkets is hiring an AI Engineer

The Role The Fastmarkets Innovation & AI team sits within the Technology function and is responsible for developing and delivering the company’s AI and data strategy through agentic AI systems, intelligent data applications, and AI-powered analytics. The team has built a modern data platform and is actively shipping AI capabilities across the business—from agentic request intake systems to natural language data exploration tools powered by LLMs and semantic models. Reporting directly into the Head of AI Enablement, we are looking for an AI Engineer to design, build, and ship AI-powered applications and features. This is a fantastic opportunity to join us and contribute to a high-performing workstream delivering transformational AI capabilities across both internal tools and monetizable data assets. You will work with cutting-edge AI technologies—Anthropic Claude, Snowflake Cortex AI, semantic models, and agentic frameworks—to build systems that enable users to interact with data and insights in entirely new ways. We actively leverage AI tools across the engineering workflow, including Anthropic Claude for code generation, design, and review, and Snowflake Cortex AI capabilities for in-platform intelligence. The successful candidate will be expected to embrace AI-assisted development as a core part of how we work. The role requires a high standard of expertise in Python and TypeScript/JavaScript, as well as specialist knowledge of LLMs, prompt engineering, agentic systems, and semantic data modelling. You should be comfortable working across the full stack—from designing LLM-powered backends to building modern React frontends. Principal Accountabilities · Design and develop agentic AI systems that solve real business problems—request intake agents, natural language query systems, intelligent data exploration tools, and AI-powered search and analysis features · Integrate LLMs into applications using best practices: prompt engineering, tool use, multi-turn conversation management, output validation, and cost optimisation · Build semantic models and data schemas that enable natural language querying over enterprise data (Snowflake Cortex Analyst, custom RAG systems) · Connect AI systems to data pipelines: design how data flows from source → transformation → semantic layer → AI application, ensuring freshness, quality, and lineage are observable · Develop full-stack AI applications: Next.js frontends with streaming chat UIs, FastAPI/Python backends for agentic workflows, and server-side integration with LLMs and data platforms · Implement observability and evaluation frameworks for AI systems—structured logging, conversation tracing, golden evals, and adversarial verification of agent behaviour · Work with modern orchestration and deployment patterns: containerised on-demand environments, Snowflake SPCS, CI/CD pipelines, and infrastructure-as-code · Write production-grade Python and TypeScript: follow code-first practices, maintain strict type safety, build testable systems with clear separation of concerns · Embrace AI-assisted development: use Claude for code generation, architecture design, and code review to accelerate delivery without compromising quality · Identify and promote best practices in AI engineering: recommend improvements to prompting strategies, evaluation methodology, data schema design, and system reliability · Excellent problem-solving skills and ability to embrace change—AI is a rapidly evolving field; you should be curious, experimental, and comfortable with ambiguity · Effective communication and collaboration skills: you’ll work cross-functionally with data engineers, product teams, and business stakeholders to translate requirements into shipped AI features · Natural self-starter, with enthusiasm for learning and research: staying current with the latest in LLMs, prompt engineering, and agentic systems is essential KEY INTERFACES Internal: · Head of AI Enablement · Data Engineering team (for data pipeline and schema design) · Technology & Infrastructure teams · Product and Commercial teams · BI & Analytics teams & stakeholders across the business External: · Anthropic technical partnerships team · Snowflake technical account team · Cloud platform providers (Azure, AWS, Snowflake) · Third-party vendor and tooling support