Forward Deployed AI Engineer H/F

Description

This role is production engineering, not consulting, project delivery or research.

As a Forward Deployed AI Engineer you work inside a client's organization alongside their engineers and business staff, making complex AI platforms function in messy real-world settings.

Success is judged by outcomes: how fast value arrives, how widely the tools are adopted, and how reliable and scalable the systems are, not by milestones ticked off.

AI products usually stall because deployment fails, not because the models are weak. Closing the gap between a working pilot and a scaled capability takes engineers who can turn platform capability into measurable business value.

These engineers form the execution core of the company's deployment engineering pods. The company is building the largest FDE capability in the services industry, so early hires will shape the role as it grows and will work on hard enterprise AI problems across industries.

The company is a global professional services firm with about 791,000 people serving clients in more than 120 countries.

Responsibilities

• Work alongside client engineering and business teams to deploy, scale and run AI platforms from providers such as Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP or Palantir within enterprise settings.
• Take ownership of production results from start to finish: time-to-value, reliability, adoption speed and scalability, tied to business metrics.
• Move quickly from a vague business problem to a working production system, prototyping in days and reaching production-ready in weeks.
• Design and oversee AI architecture across identity, data, security, governance, the platform layer and workflow integration.
• Explain technical architecture to CTOs, CFOs and CISOs in terms of business impact, and help shape use case roadmaps, ROI backlogs and AI adoption strategy.
• Build reusable patterns, playbooks and accelerators that the client keeps, so their own team can run the system without you.
• Facilitate design sessions, prototype builds and architecture briefings with client engineers and leaders, coding together with them where useful.
• Capture patterns and lessons from each engagement so they can be reused across projects and help grow the FDE practice.

Requirements

• 3-5 years of engineering experience on cloud-native systems, including APIs, microservices, containers and serverless.
• At least 1 year of hands-on work putting agentic solutions into production, covering agents, orchestration, context engineering, RAG and workflows.
• At least 3 years working with AI platforms such as OpenAI, Claude, Vertex AI and open-source models, including building abstraction layers for pipelines across several providers.
• At least 10 years deploying to production, covering CI/CD, infrastructure as code with Terraform and Helm, monitoring and debugging.
• Proof of owning delivery end to end in a client-embedded setting. Internal projects, vendor labs or deployments only for your own team do not count.
• Ability to show business value clearly, with deployment impact quantified in terms a CFO would accept.
• Experience presenting to and earning the trust of senior client stakeholders such as CTOs, CFOs or CISOs.
• Non-linear career paths are welcome. Assessment rests on deployment experience and outcome ownership rather than matching a CV pattern.

Conditions

Hiring decisions are made without regard to age, ethnicity, religion, sex, disability, sexual orientation, gender identity, citizenship or any other legally protected status.