Forward Deployed AI Engineer

Job Description

You report directly to the Head of IT Architecture as a Forward Deployed AI Engineer. You will embed directly with teams across the bank and empower employees to get the most out of the hybrid-cloud-powered Sovereign AI Platform and the broader AI toolset run by the IT Department. This is a hands-on, white-glove role focused on augmenting people in their work and helping them achieve their missionsets.

You will sit alongside employees, show them how to navigate the platform across both on-prem and public-cloud environments, and help them build real solutions tailored to their specific objectives. Acting as the bridge between the bank’s AI capabilities and the people who depend on them, you will translate mission needs into working implementations and ensure each team walks away more capable, confident, and self-sufficient.

The goal is not just adoption, it is augmentation: making every employee measurably more effective at their mission by putting the right AI tools in their hands and teaching them to wield those tools well. Success looks like teams that build on the platform independently to advance their own missionsets. Equally, you are the platform’s eyes and ears in the field — every friction point, missing capability and recurring request you encounter becomes structured feedback you carry back to the IT teams, closing the loop between what users need and how the stack evolves.

• Embed with business, engineering, data-science and executive teams to understand their missionsets, then design and prototype AI solutions on the Sovereign AI Platform that meet those needs.
• Onboard users to Open WebUI and to the governed external providers (Anthropic Claude, OpenAI GPT) via Agentgateway — prompt design, retrieval-augmented patterns, and safe, cost-aware usage.
• Help teams build, register and operate agentic workflows using kagent and the agentregistry catalog, wiring them into the systems and data sources they already use.
• Support data scientists on JupyterHub and the LLM-d inference endpoints — model selection, serving and fine-tuning patterns, and moving prototypes toward production.
• Build integrations and reference implementations that span on-prem and public-cloud environments, leaving behind reusable patterns the wider bank can adopt.
• Run hands-on enablement — pairing sessions, workshops, office hours and tailored documentation — that leaves each team able to extend its own solution without you.
• Capture friction points, capability gaps and recurring requests as structured feedback, and channel them to the IT Platform Operations, Systems (ITSS) and Cybersecurity (ITCS) teams to drive platform improvements.
• Ensure every deployed solution respects the bank’s AI governance — data sovereignty, content safety, prompt-injection defense and agentic-workflow audit — under the EU AI Act and DORA.

Qualifications

Minimum Qualifications

• 5+ years in a software, data, ML or platform engineering role, including hands-on experience building and shipping working solutions — from prototype through to production.
• Demonstrated ability to work directly with users: gathering requirements, translating business or mission needs into technical implementations, and delivering against them.
• Practical experience with large language models and modern AI tooling — prompting, retrieval-augmented generation, API integration, and at least foundational agentic patterns.
• Comfortable building across hybrid infrastructure (on-prem and public cloud) and integrating with existing enterprise systems and data sources.
• Excellent communication and teaching skills, a genuine enthusiasm for enabling others, and the ability to meet both technical and non-technical colleagues exactly where they are.

Preferred Qualifications

• Hands-on experience with one or more platform components: Open WebUI, JupyterHub, kagent, LLM-d, or comparable inference / agent frameworks.
• Experience integrating governed LLM providers (Anthropic Claude, OpenAI GPT) and building retrieval, tool-use or agentic workflows in production.
• Proficiency in Python plus at least one of Go, Rust, Java or JavaScript/TypeScript; comfortable with REST / gRPC APIs and containers (Kubernetes a plus).
• Prior forward-deployed, solutions-engineering, developer-relations or technical-consulting experience — embedding with teams to ship outcomes.
• Experience in regulated or high-assurance environments (banking, telco, aviation, pharmaceutics, government) and familiarity with AI governance (EU AI Act, DORA, GDPR).
• Strong facilitation skills — running workshops, office hours and enablement programmes — with a track record of leaving teams measurably more self-sufficient.

Additional Information

SQ2