Applied AI Engineer F/H/X

Teamwill Consulting’s CTAIO function builds and operates the firm’s reusable artificial intelligence (AI) engineering assets, then deploys them into some of the firm’s most demanding engagements.

About Teamwill Consulting

Putting domain expertise and artificial intelligence at the heart of the decisions that finance millions of projects, vehicles, and equipment around the world is the core of what we do.

For more than 20 years, Teamwill Consulting has been supporting leading players in banking, consumer finance, and mobility through their strategic and technological transformations. Operating in 11 countries and bringing together more than 800 professionals, the Group combines deep industry expertise, close client relationships, and the ability to deliver on a global scale.

Artificial intelligence is a cornerstone of our growth strategy. To carry that ambition, we are strengthening our technology, engineering, and data teams.

Role Summary

Teamwill Consulting’s CTAIO function builds and operates the firm’s reusable artificial intelligence (AI) engineering assets, then deploys them into some of the firm’s most demanding engagements. As an Applied AI Engineer, you design, build, and operate production AI systems that combine managed large language models (LLM) with managed or self-hosted, proprietary or open-weight models, under real infrastructure constraints. You turn an AI capability from a working prototype into something an engineering organization can trust in production: tested, observable, and safe to change.

Key Responsibilities

Design, build, test, and operate agentic systems and pipelines that call one or more LLMs to complete multi-step tasks. Contribute to clarifying requirements with Product Owners, Product Managers, Business Analysts, and Subject Matter Experts, and to formalizing non-functional requirements (e.g., application reliability, security, performance…).

Start with the simplest composition that works (typically applying the KISS (keep it simple and stupid) software craftsmanship principle): a direct model call or a short chain, moving to a more autonomous, tool-using agent design once a simpler one is shown to fall short. Favor a lightweight, provider-agnostic integration approach over a heavyweight orchestration framework whenever possible.

Integrate managed, application programming interface (API)-based models and self-hosted, proprietary and open-weight models into the same application, including the operational work specific to self-hosting: inference optimization and request scheduling under constrained graphics processing unit (GPU) capacity.

Build and maintain the retrieval pipelines that ground models via retrieval-augmented generation (RAG) in the client’s or firm’s knowledge bases: turning source documents into a well-structured, versioned, and access-controlled form a model can retrieve from reliably.

Build and maintain evaluation (“eval”) suites that gate every change before it reaches production (capability, safety, regression, and cost/latency thresholds). Implement guardrails (secret redaction, content-safety filtering, and fallback behavior), and instrument every AI-powered service for observability (token usage, latency, tool-call errors, and output quality) so a production issue can be diagnosed from data.

Package AI components as containerized, deployable services with documented interfaces (API, command-line interface (CLI), Model Context Protocol (MCP)), so they can be integrated into a client’s systems.

Use AI coding agents (for example Claude Code, Cursor, or OpenCode) as part of your own engineering workflow: maintain the standing context documentation a project keeps for its agents, and review agent-produced code with the same combination of automated checks and human review as code anyone else on the team writes

Work closely with clients, Data Engineers, Platform Engineers, and Forward Deployed Engineers to move an asset from prototype to a client-ready, secure, and monitored deployment, and contribute field learnings back into Teamwill’s shared asset portfolio, so a solution built for one engagement strengthens every future one.

What You’ll Work On

Your primary engagement sits inside a large, regulated financial-services environment where AI adoption is real and accelerating. Some workloads run against managed, proprietary models under a strict data-governance agreement; others run entirely on infrastructure the client controls, using open-weight models, to meet data-residency and regulatory requirements. Both paths are rich engineering challenges: the managed path calls for rigorous prompt, cost, and safety engineering, and the self-hosted path for model serving and capacity planning in a market where GPU capacity is scarce. You will have the opportunity to build expertise in both.

The work is international: you will coll