We are:
A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.
You are:
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. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.
The Work:
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.
Key Responsibilities
Lead enterprise AI platform deployments across complex multi-stakeholder client environments — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — owning the full program from architecture through adoption
Own program-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics attached
Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments
Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi-system workflow integration at program scale
Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritization frameworks, and multi-year AI adoption roadmaps
Define and publish reusable reinvention blueprints, patterns, and accelerators that scale across multiple client engagements and grow the FDE practice
Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams
Codify delivery learnings, failure patterns, and engineering standards that shape the FDE practice and enable the next generation of forward deployed engineers
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
What You Need:
Minimum of 10 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 2 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 programming in Python, Java, or equivalent; familiarity with evaluation tooling, logging, monitoring, and agent observability.
Minimum of 10 years of experience deploying to production — CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
Minimum of 10 years of experience in client communication and collaboration, including being capable of leading technical workshops and delivering under ambiguity.
Bachelor's degree in Computer Science, Engineering, or equivalent or (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience), AI certifications or agentic tool experience is a plus.
Bonus Points If:
You’ve served as an Agentic AI Engineer in an Enterprise environment
You’ve defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures.
You understand the AI-native paradigm — blending cloud-native with generative model architectures — optimizing for performance, modularity, and efficiency.
You’ve delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs.
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 10/16/2026.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits | Accenture
Role Location Annual Salary Range
California $132,500 to $366,300
Cleveland $122,700 to $293,000
Colorado $132,500 to $316,400
District of Columbia $141,100 to $337,000
Illinois $122,700 to $316,400
Maine $112,900 to $269,600
Maryland $132,500 to $316,400
Massachusetts $132,500 to $337,000
Minnesota $132,500 to $316,400
New York $122,700 to $366,300
New Jersey $141,100 to $366,300
Virginia $122,700 to $337,000
Washington $141,100 to $337,000
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