AI Engineering Associate Director

About Accenture Data & AI

A new era for Data & AI is here—one that will transform work and society. Accenture is advancing into this future with a focused strategy and clear purpose: helping clients optimise and reinvent their businesses with data and AI, supported by a $3 billion investment and a commitment to industry-leading work.

With more than 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 forefront of what’s possible.

About the Role

The AI Engineer - Associate Director is a senior leader who brings strong AI engineering expertise alongside strategic and commercial judgment to drive enterprise-wide transformation. The role requires top-tier client engagement: working independently with CXOs and board-level sponsors to set the AI transformation agenda, build the business case, and lead programmes that deliver it.

Associate Directors operate as Forward Deployed Engineers at the highest level—capable of engaging a CEO or CTO, diagnosing the business challenge, and designing and directing the build of an AI solution that solves it. They also stay current with AI developments, preserving the technical depth needed to govern architecture standards, direct senior engineers credibly, and assess the most significant technology trade-offs. They serve as portfolio leaders, practice builders, and commercial drivers at the same time.

This position is not suited to candidates who have moved away from engineering. The expectation is a leader who remains technically credible, commercially accountable, trusted by clients at the highest level, and committed to building and developing the next generation of AI engineers and the practice they support.

Position Responsibilities

CXO Engagement and Business Transformation Strategy

• Independently engage at CXO and board level—leading executive discussions on AI transformation strategy, building trusted advisory relationships, and influencing strategic technology investment decisions
• Diagnose complex business problems as a forward-deployed engineer: facilitate strategic workshops, translate business process analysis into AI transformation opportunities, and design end-to-end AI solutions that address root causes rather than symptoms
• Develop and own enterprise-level value hypotheses: build rigorous business cases using financial modelling, baseline current-state performance, and commit to measurable outcomes that can be monitored through delivery and after deployment
• Define the client’s AI transformation roadmap—prioritising initiatives based on strategic value and delivery readiness, managing board-level change, and enabling sustained organisational adoption of AI

Enterprise Solution Architecture and Technical Direction

• Set enterprise AI architecture principles and make high-impact technology choices for programmes—covering model selection, data infrastructure, serving architectures, MLOps discipline, and governance Maintain sufficient hands-on depth to challenge assumptions, evaluate trade-offs, and lead senior engineers credibly
• Maintain technical standards across the portfolio: ML development patterns, data pipeline architecture, model serving approaches, evaluation frameworks (benchmark datasets, performance metrics, A/B testing), and production readiness criteria—ensuring engineering disciplines are embedded rather than improvised
• Stay personally up to date with AI developments—assessing new frameworks, models, and tooling to determine what is enterprise-ready for production, and ensuring the practice’s delivery approach reflects leading-edge capabilities

Portfolio Governance and Delivery Excellence

• Lead a portfolio of simultaneous AI programmes—maintain clear oversight of scope, quality, risk, and commercial results, and intervene decisively when escalation or course correction is needed
• Take ownership of programme-level commercial accountability: deliver against contracted milestones, manage margin, handle change control, and ensure client satisfaction at the highest level
• Set and enforce portfolio delivery standards—from engineering rigour and MLOps 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—turn project delivery into multi-year strategic partnerships through demonstrable impact and senior-level trust
• Lead bids for major new opportunities: develop win strategies, direct solution design, write and present proposals, and participate in commercial negotiations at the most senior level
• Identify and develop new market opportunities for Accenture’s AI capability—recognise where technology is ready to generate step-change value in specific industries or functions, and mobilise the practice to pursue them

Practice Building, Hiring, and Leadership

• Build, hire, and develop a high-performing team of Senior Managers, Managers, and engineers—define role profiles, lead key hiring decisions, manage performance, and create an environment where exceptional people choose to stay and grow
• Shape Accenture’s AI practice—define capability strategy, build the IP (reference architectures, delivery frameworks, value engineering tools, and reusable accelerators), and set the standards the practice operates by
• Serve as a change leader: champion AI adoption within Accenture and with clients, motivating teams, clients, and the market with a clear and credible vision of where enterprise AI is headed and how to get there

Innovation Leadership and Market Positioning

• Contribute to Accenture’s point of view on AI: support published thought leadership, represent the practice at industry events, engage with the research community, and strengthen Accenture’s reputation as a leading enterprise AI partner
• Promote continuous learning and innovation across the practice—ensure new advances in AI research, tooling, and engineering practices move quickly from the frontier into client delivery
• Build strategic partnerships with platform vendors, hyperscalers, and AI framework providers to enhance Accenture’s capability and market competitiveness

Travel may be required for this role. Travel level can range from 0% to 100%, depending on client requirements and business needs.

Why This Role

This role is designed for individuals who have already proven they can lead at the highest level—technically, commercially, and with clients—and who want to do so in the most consequential domain in enterprise technology today. You will define what enterprise AI looks like, build the team and practice that delivers it, and drive measurable transformation for clients at the most senior level. If you have the technical credibility, client trust, and motivation to build something that matters at scale, this is where that work belongs.

Accenture is an Equal Opportunity Employer. All employment decisions shall be made without regard to age, race, creed, colour, religion, sex, national origin, disability, or any other basis protected by applicable law.

Core Pillars: CXO Engagement | Enterprise Transformation | Portfolio Leadership | BD & Growth

Core Requirements

• 3+ years delivering AI systems in production—with verifiable evidence of enterprise-scale business value created
• Experience operating as a recognised external thought leader in AI—published work, conference keynotes, or advisory board participation
• 3+ years building AI/ML applications—with deep, current understanding of the full AI technical stack: data infrastructure, feature engineering, model training, serving, monitoring, and MLOps
• 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
• Hands-on technical depth sufficient to set architecture standards, govern engineering quality across a portfolio, and direct senior engineers and architects credibly on major technical decisions
• Experience with strategic vendor and partner relationships—hyperscalers, ML platforms, AI framework companies—in the context of enterprise delivery
• Proven experience engaging clients at CXO level—independently managing C-suite relationships, influencing strategic technology decisions, and building board-level trust
• Experience delivering in two or more regulated industries (financial services, healthcare, telecoms, energy) where compliance, data residency, and AI governance shape architecture at the deepest level
• Proven experience leading enterprise AI transformation programmes—value hypothesis development, business case construction, process reengineering, and outcome measurement at scale
• Experience delivering global programmes—multi-country, multi-entity, multi-language AI programmes with complex governance structures
• 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
• 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
• 14+ years of full stack engineering and software delivery—sustained technical depth with active, current engagement in AI and broader technology developments
• 10+ years cloud-native development—deep understanding of enterprise cloud architecture, infrastructure, DevOps, and production operations at scale
• Proven ability to attract, hire, and retain exceptional AI engineering talent—recognised by candidates as someone they want to work for
• 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

Additional Strong Signals

• Experience with advanced ML techniques: deep learning, NLP, computer vision, generative AI, or reinforcement learning at enterprise scale
• Background in data science, analytics, or AI research prior to engineering leadership
• Experience with responsible AI: fairness assessment, bias mitigation, explainability, and governance frameworks
• Bachelor's degree in a related field. A Master's degree in Computer Science, AI, Engineering, or an MBA is highly valued.