Forward Deployed AI Engineer

Accenture PLC; München; Presencial; EUR 110.000 - 150.000; Jornada completa

Descripción de la vacante

Accenture PLC is seeking a Forward Deployed AI Engineer to join our Reinvention Deployment Engineering team. This role embeds with client engineering and business teams to deploy AI platforms inside enterprise environments, taking ownership of outcomes such as time-to-value, reliability, adoption velocity, and scalability.

You will move rapidly from ambiguous business problems to production-ready solutions, design and govern AI architectures across the enterprise stack, and translate technical

Formación

• 3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
• 1+ year designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production.
• 3+ years with AI platforms (OpenAI, Claude, Vertex AI) including multi-provider pipelines.
• 10+ years deploying to production with CI/CD and IaC (Terraform, Helm).
• Demonstrated end-to-end delivery ownership in a client-embedded environment.
• Proven ability to articulate business value to CFO-level stakeholders.
• Experience presenting to senior client stakeholders (CTO, CFO, CISO).
• Non-linear profiles welcomed; assessment based on deployment experience and outcome ownership.

Responsabilidades

• Embed with client teams to deploy, scale, and operationalize AI platforms inside enterprise environments.
• Own production outcomes end-to-end with time-to-value, reliability, adoption velocity, and scalability.
• Translate business problems into production-ready systems via rapid prototyping.
• 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.
• 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.

Conocimientos

Cloud-native systems

Agentic solutions design

AI platforms

CI/CD & IaC

End-to-end delivery

Stakeholder communication

Herramientas

Terraform

Helm

Descripción del empleo

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 havedemonstrated: 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 scalesisbridged 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

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