Forward Deployed AI Engineer

Accenture DACH; München; On-site; EUR 90,000 - 130,000; Full time

Job summary

Accenture DACH is seeking a Forward Deployed AI Engineer who embeds with client teams to deploy, scale, and operationalize AI platforms inside enterprise environments. You own outcomes such as time-to-value, adoption, reliability, and scalability, not just milestones.

In this role you will design AI architectures across the enterprise stack, translate technical work into business value for CIOs, CFOs, and CISO, and lead workshops with client leaders.

Qualifications

• 10+ years deploying to production
• 3-5 years engineering with cloud-native systems
• 1 year deep expertise in agentic solutions (agents, orchestration, context engineering, RAG) in production
• Minimum 3 years with AI platforms and open-source models

Responsibilities

• Embed with client engineering and business teams to deploy, scale, and operationalize AI platforms inside enterprise environments
• Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, scalability with business metrics
• Move from ambiguous business problem to working production system through rapid prototyping
• Design and govern AI architectures across identity, data, security, governance, and workflow integration
• Translate technical architecture into business impact for client leadership; shape use case roadmaps and ROI backlogs
• Build reusable patterns and accelerators that the client owns after you leave
• Lead design workshops, proofs of concept, architecture walkthroughs with client teams
• Codify patterns and delivery learnings for scale across engagements

Skills

AI deployment

Production engineering

Cloud-native

CI/CD

Terraform

Helm

RAG

Architectures

Business value

Tools

APIs

Microservices

Containerization

Job description

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.

Key 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.
• Minimum of 10 years of 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