Senior Python / Agentic AI Engineer

Senior Python / Agentic AI Engineer

Capco: Where Innovation Meets Opportunity

Capco is a global leader in consulting, uniquely positioned to drive innovation and transformation in the financial services industry. More than consultants, we are strategic partners and problem solvers for some of the largest organizations in Banking, Insurance, Investment Management, Data/Digital Transformation, Regulatory Compliance, and ICT Risk. Our business and technology professionals thrive in a dynamic environment where creativity, collaboration, and cutting-edge solutions are at the heart of everything we do. Experience the Capco difference and explore how you can shape your future with us at capco.com.

Capco’s AI Lab

The AI Lab is a cross-disciplinary research and engineering centre that translates advanced AI research into robust, deployable solutions for clients and internal products. It runs applied research (ML and agentic/LLM systems, planning, simulation and evaluation), rapid prototyping and production engineering (APIs, CI/CD, observability, data platforms and cost/latency optimisation), and rigorous evaluation and safety work (benchmarks, red-teaming, monitoring, governance and compliance). The lab also supports client engagements and internal innovation by building demonstrators and reference architectures, transferring technology to delivery teams, publishing technical learnings, and maintaining the shared infrastructure, tooling and best practices that enable reproducible, secure and scalable AI deployment.

Responsibilities

• Design and implement scalable, production-grade Python services that leverage LLMs and Agentic architectures.
• Design and implement robust agent workflows and orchestration patterns to automate complex business processes in regulated enterprise environments.
• Build and maintain reliable AI-enabled backend systems, including orchestration layers, tool integrations, knowledge access mechanisms, and observability.
• Design modern knowledge access architectures for LLM systems, including retrieval strategies, semantic indexing, and structured interfaces to enterprise data sources.
• Apply strong software engineering practices (testing, CI/CD, monitoring, structured