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The Role  We are looking for a Software Engineer to join the Fundamental Equity COO team, embedded directly within the business rather than in a central technology function. You will work alongside quant researchers and COO management to build backend services, data pipelines, and internal APIs that improve productivity, deepen AI adoption, and make better use of the data the business already has access to. AI integration is a core part of this role, not an afterthought. We are actively building out how LLMs, agentic workflows, and AI-powered tooling fit into the investment process, and this hire is expected to drive a meaningful part of that. You should come in with both hands-on experience and genuine conviction about where these technologies are heading. The role sits close to the investment process. We are looking for someone proactive and delivery-focused: someone who identifies what needs to be built, takes ownership, and gets it done without waiting to be directed. The expectation is high-quality output, pragmatic solutions that work in the hands of investment professionals and create immediate value. What you’ll do Write clean, well-structured, maintainable code and contribute to good engineering practices within the team, including CI/CD pipelines and version control, with an active contribution to firmwide best practices so that deliverables can be broadly leveraged across the business Build, deploy, and maintain internal APIs (FastAPI) and backend services that surface portfolio analytics, market data, and quantitative outputs to investment professionals, hosted within the team’s AWS environment Own and extend scheduled data pipelines and orchestration workflows (Prefect) running in containerized environments (Docker, Kubernetes), ensuring reliability and observability across the platform Contribute to the AI integration layer in close coordination with central Technology teams: strategically implementing and operating AI capabilities tailored to the specific needs of the Fundamental Equity business, spanning LLM APIs, MCP servers, and agentic workflows across a multi-model architecture, with the awareness that this is a rapidly evolving landscape requiring continuous reassessment of what best looks like Build and maintain a shared AI plugin and skills library that can be leveraged by all Fundamental Equity investment professionals, ensuring capabilities are well-documented, reusable, and model-agnostic Develop and own structured databases (PostgreSQL) underpinning research, portfolio, and operational workflows, with a view to making that data increasingly accessible and actionable through AI What you’ll bring What you need: 2–5 years of experience in a relevant role: quantitative development, dev strats, analytics engineering, or quantitative analysis at a bank, hedge fund, asset manager, or financial data provider Strong Python proficiency with an emphasis on well-structured, object-oriented codebases; solid SQL and database design skills, particularly PostgreSQL Experience building and deploying backend services and APIs (FastAPI or equivalent) in a production environment Hands-on experience with AI/LLM integration across multiple model providers: API usage, prompt engineering, tool use, and retrieval-augmented workflows. You have built things with these, not just read about them Practical familiarity with MCP servers, agentic frameworks, or multi-model orchestration architectures (custom implementations or equivalent) Experience with AI-assisted development tools (e.g. Claude Code, Cursor, or similar) as part of a day-to-day engineering workflow Comfortable working within AWS: EC2, containerized workloads, and infrastructure managed via Terraform or similar IaC tooling Experience with Docker, Kubernetes, and workflow orchestration frameworks (Prefect, Airflow, or equivalent) Sound software engineering fundamentals: version control, structur