This is not a consulting role. It is not a project delivery role. It is not a research position. A Forward Deployed AI Engineer is a production engineer who works embedded inside a client's enterprise, shoulder to shoulder with their teams, to make complex AI platforms work in real, messy organizational environments. You own outcomes: time-to-value, adoption, reliability, and scalability. Not delivery milestones. Outcomes.

The market is beginning to understand what leading technology companies have demonstrated: AI products fail not because the models are weak but because deployment is broken. The gap between a successful AI pilot and an AI capability that scales is bridged by engineers who can translate platform capability into measurable business value inside a real enterprise environment. That is this role.

Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.

Responsibilities

• Embed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environments.

• Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones.

• Move from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready.

• Design and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration.

• Translate technical architecture into business impact for client CTO, CFO, and CISO; shape use case roadmaps, ROI backlogs, and AI adoption strategy.

• Build reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you

• Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams.

• Codify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice.

• 3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
• Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
• Minimum of 3 years of experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
• Extensive experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
• Demonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify.
• Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on.
• Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level.
• Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching.
• Very good German and English language skills, as well as a willingness to travel occasionally for business purposes.

What we offer

• The opportunity to architect and deliver AI-powered solutions for leading global enterprises across industries, technologies, and complex transformation programs;
• Access to cutting-edge AI ecosystems and strategic technology partnerships, including leading cloud and AI platforms, alongside collaboration with highly experienced engineering teams;
• Clear pathways to technical leadership, specialist career tracks, mentoring opportunities, and continuous professional development through certifications and advanced learning programs;
• Flexible working models and hybrid work options that support sustainable work-life balance and individual ways of working;
• Competitive rewards and additional financial benefits, including bonus programs, employee share purchase opportunities, and other role-specific benefits where applicable.

#LI-EU

Kronberg

Berlin

Dresden

Dusseldorf

Essen

Hamburg

Jena

Mannheim

Munich

Siegburg

Stuttgart

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