Applied AI Engineer

Responsibilities:

• AI-Powered Feature Development: Build LLM-powered features directly into client-facing platforms, including research intelligence tools, natural language query layers, automated summarisation, and agentic workflows that fundamentally change how investment teams work.
• Agentic Tooling and MCP Integration: Design and implement MCP-connected data sources, agentic pipelines, and AI orchestration layers using frameworks like Claude Code, LangGraph, Open Claw, Open Code and similar, extending client platforms with live, intelligent data access.
• Full-Stack Application Development: Build end-to-end applications tailored to each client's unique portfolio analytics, risk management, and research workflows from backend APIs to responsive frontends.
• Backend Services: Design and maintain high-performance APIs using Python (FastAPI or similar) that power client-specific data access, analytics, and AI inference.
• Frontend Development: Build intuitive, responsive user interfaces in React that enable investment teams to interact with complex financial data clearly and efficiently.
• Data Pipeline Development: Build and maintain ETL pipelines that handle critical financial market data- positions, securities, risk metrics, and research signals with reliability and performance.
• Financial Analytics: Implement analytics layers for performance and risk calculations using time series and linear algebra operations (Pandas, Polars).
• Ship Fast, Iterate Often: Deliver working software in compressed timelines, gather direct feedback from hedge fund users, and continuously improve, treating speed and quality as complementary, not competing.
• Kubernetes Deployments: Be able to work fluidly with Kubernetes within each client environment to be able to ship fast and reliably.

Requirements:

• 3+ years of software engineering experience spanning both backend and frontend development.
• Fundamental understanding of the agentic loop that is used within most agent frameworks such as Codex, Claude Code, Open Code, Cline, etc.
• You understand from first principles how this works.
• Strong Python skills with hands-on experience building APIs using FastAPI, Flask, or Django as well as CLIs with click, argparse, etc.
• Frontend development experience with modern frameworks, particularly React.
• Solid understanding of RESTful APIs, data modelling, and secure API design.
• Experience with SQL and analytical libraries (Polars, Pandas) for computational workloads.
• Exposure to cloud platforms (Azure, AWS, or GCP) and cloud-native architectures.
• Experience with containerised development (Docker) and deployment workflows.
• Passion for AI: Genuine conviction that AI is transforming software, demonstrated through active use of AI tools in your development workflow (Claude, GitHub Copilot, Cursor, or similar) and curiosity about what comes next.
• Deep interest in finance: Strong desire to understand how institutional investors, hedge funds, and asset managers think, make decisions, and use technology; you find the domain genuinely compelling, not just a backdrop.
• Excellent communication skills and comfort translating technical concepts to non-technical stakeholders.
• Strong problem-solving instincts and comfort operating with ambiguity.
• Experience: Hands-on experience building with LLM APIs, agentic frameworks (Claude Code, OpenClaw, LangChain), prompt engineering, MCP servers beyond just using AI tools as a developer.
• Experience with financial data systems, portfolio analytics, or risk platforms; able to work fluently with data models like positions, securities, factor exposures, and P& L.
• Familiarity with Databricks, Delta Lake, or Auto Loader for scalable data infrastructure.
• Experience with ETL orchestration tools (Airflow, Dagster, Prefect) and data transformation frameworks (DBT).
• Experience with OLAP databases (Snowflake, ClickHouse, DuckDB, MSSQL).
• Track record of building and shipping client-facing applications on tight timelines.
• Understanding of distributed system design, event-driven architectures, and performance optimisation.
• Bachelor's or master's degree in computer science, engineering, or a comparable subject.