Asks for

The ad says“Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.”
The ad says“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.”
The ad says“3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).”
The ad says“Minimum of 10 years of experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.”
The ad says“3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).”
The ad says“3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).”
The ad says“Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.”
The ad says“Minimum of 10 years of experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.”
The ad says“Minimum of 10 years of experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.”
The ad says“Minimum of 10 years of experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.”
The ad says“3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).”
The ad says“Minimum of 10 years of experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.”
The ad says“Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.”
The ad says“Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.”
The ad says“3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).”
The ad says“Minimum of 10 years of experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.”
The ad says“Minimum of 1 years of deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.”

Role 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

Basic Qualifications

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

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us at www.accenture.com

Equal Employment Opportunity Statement

We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, sexual orientation, gender identity or expression, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.