Forward Deployed AI Engineer - Senior Manager

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

• •Own account-level AI transformation across the client enterprise — from platform selection and architecture through to enterprise-wide adoption and reinvention at scale, leading enterprise AI platform deployments (Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir) across complex, multi-stakeholder client environments and owning the full programme from architecture through adoption and ongoing iteration
• •Hold full accountability for deployment outcomes at account level: quality, time-to-value, reliability, adoption velocity, scalability, and ROI across multiple concurrent workstreams, with commercial metrics attached — directly reported to client executive leadership

Drive ambiguity to resolution at account scale: translate executive-level strategic intent into production AI systems operating across the enterprise

• •Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments through hands-on prototyping, iteration, and deployment of AI-enabled workflows, agents, copilots, and automations into real operational environments
• •Define enterprise AI architecture standards and governance frameworks across the client organization, spanning identity, data, security, platform layer, multi-platform integration, multi-system workflow integration at programme scale, and AI lifecycle management; architect and govern enterprise AI solutions across the full technology stack, including prompt and workflow design, API integrations and orchestration, enterprise knowledge integration, and privacy considerations; provide technical leadership and best practices for responsible, secure, and scalable AI deployment
• •Own the relationship with client CTO, CFO, and CISO: shape AI investment strategy, board level narrative, and multi-year reinvention roadmap; build value architecture, ROI backlogs, use case prioritization frameworks, and multi-year AI adoption roadmaps
• •Codify delivery learnings, failure patterns, and engineering standards into reusable reinvention blueprints, patterns, accelerators, and playbooks that define what enterprise AI reinvention looks like — assets that scale across markets, industries, and the FDE practice globally; grow the FDE practice and enable the next generation of forward deployed engineers
• •Lead executive workshops, board-level engagements, architecture design sessions, code-with sessions with client engineering and C-suite leadership teams, and cross-practice architecture reviews that shape the client’s AI reinvention trajectory
• •Work directly with clients and internal teams to understand business problems, shape AI use cases, and define technical solutions from ambiguous or evolving requirements; serve as the technical bridge between product, engineering, data, and business stakeholders, ensuring solutions are technically sound, usable, and aligned with business value
• •Design and implement end-to-end AI solutions, integrating LLMs and AI services such as OpenAI, Anthropic, and Gemini with enterprise platforms including SAP, ServiceNow, Salesforce, data platforms, and internal systems

At Accenture, we hire the best people and help them become even better through world-class training, career counseling, mentoring and support to help you maintain professional success.

Travel may be required for this role. The amount of travel will vary from 25% to 75% depending on business need and client requirements.

Basic Qualifications

Required qualifications

Bachelor’s degree or college diploma in a related field.

• •10–15 years of engineering experience with cloud-native systems, including APIs, microservices, containerization, and serverless architectures.
• •Minimum of 8 years of software engineering experience using Java, TypeScript/JavaScript, or Python
• •Minimum of 8 years of experience integrating APIs and building distributed, production systems

Minimum of 1 year of experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments

• •Minimum of 1 year of hands-on experience applying AI-native and agentic software development practices across the software development lifecycle, including spec-driven development and the use of AI coding agents to accelerate software design, coding, testing, debugging, modernization, documentation, and delivery.
• •Minimum of 1 years of experience working with AI platforms, including OpenAI, Claude, Vertex AI, or open-source models, with experience working with enterprise AI platforms such as Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir

Experience building abstraction layers to manage multi-provider AI pipelines

• •10–15 years of experience leading, managing, and developing software engineering teams, including delivery oversight, resource allocation, performance management, development planning, and professional development of direct reports
• •Minimum of 8 years of experience in client-facing communication and collaboration, including leading technical discussions, workshops, or delivery sessions under ambiguity, with experience leading executive workshops, architecture design sessions, or cross-functional technical engagements
• •Minimum of 8 years of experience translating high-level business needs into concrete technical designs and implementations in large enterprise-wide organizations or a professional services firm
• •Demonstrated end-to-end delivery ownership in a client-embedded environment, including experience leading enterprise AI deployments across multi-stakeholder environments

Experience working directly with enterprise clients on AI deployment initiatives

Experience defining enterprise AI architecture standards, including identity, data, security, governance, multi-system integrations, and AI lifecycle management

Experience working with executive stakeholders (CTO, CFO, CISO) on AI strategy and investment decisions

Experience leading executive workshops, board-level engagements, architecture design sessions, or cross-functional architecture reviews

• •Experience developing reusable deployment frameworks or playbooks
• •Experience contributing to the development of engineering standards, practice models, or capability frameworks

• 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, based on full-time
• employment, for roles that may be hired as set forth below.
• The recruiting efforts for this position are intended to fill a brand new position.
• The base pay range sh