AI Engineering Associate Director

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 AI Engineer - 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 an AI solution that solves it. They remain personally current with developments in 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 AI 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 AI across the organisation

Enterprise Solution Architecture and Technical Direction

• Set architecture principles and make consequential technology choices for enterprise AI programmes - spanning model selection, data infrastructure, serving architectures, MLOps discipline, and governance maintain sufficient hands-on depth to challenge assumptions, evaluate trade-offs, and direct senior engineers credibly
• Govern technical standards across the portfolio: ML development patterns, data pipeline architecture, model serving strategies, evaluation frameworks (benchmark datasets, performance metrics, A/B testing), and production readiness criteria - ensuring engineering disciplines are institutionalised, not improvised
• Stay personally current with 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 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 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 - 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 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 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 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 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 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

Travel may be required for this role. The extent of travel will vary from 0% to 100% depending on client requirements and business need.

Why This Role

This role is for those who have already demonstrated that they can lead at the highest level - technically, commercially, and with clients - and who want to do it 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 create measurable transformation for clients at the most senior level. If you have the technical credibility, the client trust, and the drive to build something that matters at scale, this is where that work happens.

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 - 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 consequential technical decisions
• Experience with strategic vendor and partner relationships - hyperscalers, ML platforms, AI framework companies - in the context of enterprise delivery
• Demonstrated track record of CXO-level client engagement - independently managing C-suite relationships, influencing strategic technology decisions, and building board-level trust
• 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
• Proven experience leading enterprise AI transformation programmes - value hypothesis development, business case construction, process reengineering, and outcome measurement at scale
• Experience with global programme delivery - 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
• A 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.