About Accenture Data & AI

The beginning of a new Data & AI decade that will reshape work and society is underway. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimise and reinvent their businesses with data and AI — backed by a $3 billion investment and a commitment to industry-defining work.

With over 45,000 professionals dedicated to Data & AI, Accenture's Data & AI organisation brings together Experienced Innovation, Strategic Investment, Exceptional Talent, and a Power Ecosystem to deliver outcomes at the frontier of what is possible.

About The Role

The Full Stack AI Associate Director is a senior leader who combines deep AI engineering credibility with the strategic and commercial acumen to drive enterprise-scale transformation. This role operates at the highest level of client engagement — working independently with CXOs and board-level sponsors to define the AI transformation agenda, build the business case, and lead the programmes that deliver it.

Associate Directors are Forward Deployed Engineers at the most senior level — able to engage a CEO or CTO, diagnose the business problem, and design and direct the build of a full stack AI solution that solves it. They remain personally current with developments in agentic AI, maintaining the technical depth to govern architecture standards, direct senior engineers credibly, and evaluate the most consequential technology trade-offs. They are simultaneously portfolio leaders, practice builders, and commercial drivers.

This is not a role for those who have left engineering behind. The expectation is a leader who is technically credible, commercially accountable, client-trusted at the most senior level, and driven to build and develop the next generation of AI engineers and the practice they work in.

Position Responsibilities

CXO Engagement and Business Transformation Strategy

Engage independently at CXO and board level — leading executive conversations on AI transformation strategy, building trusted advisory relationships, and influencing strategic technology investment decisions.

Diagnose complex business problems as a forward-deployed engineer: facilitating strategic workshops, synthesising business process analysis into AI transformation opportunities, and designing end-to-end agentic solutions that address root causes, not symptoms.

Develop and own enterprise-level value hypotheses: constructing rigorous business cases with financial modelling, baselining current-state performance, and committing to measurable outcomes that can be tracked through delivery and post-deployment.

Define the client's AI transformation roadmap — prioritising initiatives by strategic value and execution readiness, managing board-level change, and ensuring sustainable adoption of agentic AI across the organisation.

Enterprise Solution Architecture and Technical Direction

Set architecture principles and make consequential technology choices for enterprise agentic AI programmes — spanning agent harness design, orchestration patterns, knowledge layer strategy, trust and safety frameworks, and AgentOps discipline; maintain sufficient hands-on depth to challenge assumptions, evaluate trade-offs, and direct senior engineers credibly.

Govern technical standards across the portfolio: orchestration and A2A patterns, knowledge layer architecture (RAG, MCP, Text-to-SQL, knowledge graphs), evaluation frameworks (golden datasets, LLM-as-judge, trajectory evals), and production readiness criteria — ensuring engineering disciplines are institutionalised, not improvised.

Stay personally current with agentic AI developments — evaluating new frameworks, models, and tooling; determining what is ready for enterprise production; and ensuring the practice's delivery approach reflects the leading edge of what is possible.

Portfolio Governance and Delivery Excellence

Lead a portfolio of concurrent agentic AI programmes — maintaining visibility across scope, quality, risk, and commercial performance; intervening decisively when escalation or course correction is required.

Own programme-level commercial accountability: delivery against contracted milestones, margin management, change control, and client satisfaction at the highest level.

Establish and enforce delivery standards across the portfolio — from engineering rigour and AgentOps discipline to value measurement and client communication — that define what excellent delivery looks like.

Sales Growth and Business Development

Own and grow a portfolio of client relationships — developing accounts from project delivery into multi-year strategic partnerships through demonstrable impact and senior trust.

Lead pursuit efforts for significant new opportunities: shaping win strategies, directing solution design, authoring and presenting proposals, and engaging in commercial negotiations at the most senior level.

Identify and develop new market opportunities for Accenture's agentic AI capability — spotting where the technology is ready to create step-change value in specific industries or functions and mobilising the practice to pursue them.

Practice Building, Hiring, and Leadership

Build, hire, and develop a high-performing team of Senior Managers, Managers, and engineers — defining role profiles, leading senior hiring decisions, managing performance, and creating an environment where exceptional people choose to stay and grow.

Drive the development of Accenture's Full Stack AI practice: defining the capability strategy, building the IP — reference architectures, delivery frameworks, value engineering tools, and reusable accelerators — and establishing the standards that the practice operates to.

Act as a change leader: championing the adoption of agentic AI both within Accenture and with clients; inspiring teams, clients, and the market with a clear and credible vision of where enterprise AI is going and how to get there.

Innovation Leadership and Market Positioning

Shape Accenture's point of view on agentic AI: contributing to published thought leadership, representing the practice at industry events, engaging with the research community, and building Accenture's reputation as the leading enterprise agentic AI partner.

Champion continuous learning and innovation across the practice — ensuring that new developments in AI research, tooling, and engineering practice flow rapidly from the frontier into client delivery.

Identify strategic partnerships with platform vendors, hyperscalers, and AI framework providers that strengthen Accenture's capability and competitive position in the market.

Core Requirements

3+ years delivering agentic AI systems in production — with verifiable evidence of enterprise-scale business value created.

3+ years building LLM-based applications — deep, current understanding of the full agentic AI technical stack: harness, orchestration, MCP/tools, knowledge layer (RAG, Text-to-SQL, knowledge graphs), evaluation, trust and safety, and AgentOps.

Hands-on technical depth sufficient to set architecture standards, govern engineering quality across a portfolio, and direct senior engineers and architects credibly on consequential technical decisions.

Demonstrated track record of CXO-level client engagement — independently managing C-suite relationships, influencing strategic technology decisions, and building board-level trust.

Proven experience leading enterprise AI transformation programmes — value hypothesis development, business case construction, process reengineering, and outcome measurement at scale.

12+ years experience in classical AI/ML, data engineering, and advanced analytics — with a track record of delivering intelligent systems at scale in complex, regulated enterprise environments.

14+ years of full stack engineering and software delivery: sustained technical depth with active, current engagement in agentic AI and broader AI developments.

10+ years cloud-native development — deep understanding of enterprise cloud architecture, infrastructure, DevOps, and production operations at scale.

Expert-level experience in digital transformation and complex enterprise software delivery across large, regulated, or multi-national environments.

10+ years of technical and delivery leadership — portfolio management, commercial accountability, and team development at senior levels.

Proven track record of sales and account growth: pursuing and winning significant new business, growing existing accounts, and contributing to revenue targets.

Experience hiring and developing senior engineering talent in competitive markets.

Bachelor's degree in a related field. A Master's degree in Computer Science, AI, Engineering, or an MBA is highly valued.

Additional Strong Signals

Experience operating as a recognised external thought leader in agentic AI — published work, conference keynotes, or advisory board participation.

Experience building or significantly scaling an AI engineering practice — defining capability strategy, hiring at scale, and developing the IP and standards the practice runs on.

Experience with strategic vendor and partner relationships — hyperscalers, LLM providers, AI framework companies — in the context of enterprise delivery.

Experience in two or more regulated industries (financial services, healthcare, telecoms, energy) where compliance, data residency, and AI governance shape architecture at the deepest level.

Experience with global programme delivery — multi-country, multi-entity, multi-language agentic AI programmes with complex governance structures.

Deep familiarity with enterprise AI economics: total cost of ownership modelling, build vs buy decisions, make vs partner strategies, and AI investment governance at board level.

Experience navigating complex organisational change associated with agentic AI adoption — workforce transformation, process redesign, and stakeholder management across large organisations.

A proven ability to attract, hire, and retain exceptional AI engineering talent — recognised by candidates as someone they want to work for.

Full Stack AI Engineer Associate Director