AI Engineer – Associate Director

AI ENGINEER – CONSULTANT

AI Engineer – Consultant
Data & AI | Accenture

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 (Consultant) is a hands-on engineer at the core of Accenture’s AI delivery capability. This role is for those who build intelligent systems — machine learning pipelines, AI-powered applications, data-driven solutions, and AI integrations — and who want to do that work at the frontier of enterprise AI. Consultants work within delivery teams, turning business requirements into production-grade AI solutions under the guidance of technical leads and managers.

The expectation is active, daily engineering: writing code, building machine learning models, designing AI pipelines, implementing integrations, and maintaining the MLOps and DevOps practices that keep production systems running. Consultants on this team are also expected to invest continuously in learning — AI is evolving rapidly, and staying current is a professional responsibility, not optional.

Position Responsibilities

AI Application Development

• Design and build AI-powered applications — Python backends, REST and event-driven APIs, and frontend interfaces — to production engineering standards
• Implement AI application workflows: model serving, prediction pipelines, real-time inference, and integration with business systems
• Translate business requirements into technical specifications; work with managers and clients to clarify scope, surface ambiguities, and deliver against agreed outcomes

Machine Learning Development

• Build, train, and deploy machine learning models using established frameworks (PyTorch, TensorFlow, scikit-learn, LightGBM): feature engineering, model selection, hyperparameter tuning, and lifecycle management
• Implement appropriate ML techniques for different use cases: supervised learning, unsupervised learning, time series forecasting, classification, and regression
• Design and maintain model versioning, experimentation tracking, and reproducible training pipelines using MLOps tools

Data Engineering & Pipelines

• Design and implement data pipelines: ingestion, transformation, enrichment, and preparation at scale
• Build ETL/ELT workflows connecting data sources to ML training and inference systems
• Implement data quality checks, validation, and monitoring to ensure data integrity throughout the pipeline

Model & System Integration

• Integrate trained models into production systems with robust error handling, monitoring, and rollback capabilities
• Design and implement model serving architectures: batch prediction, real-time APIs, and edge deployment where required
• Build ML monitoring and observability: prediction drift detection, model performance tracking, and automated retraining triggers

MLOps & Production Excellence

• Maintain CI/CD pipelines for model code, training scripts, and infrastructure — including automated testing and deployment strategies
• Instrument ML systems with production observability: latency tracking, prediction profiling, model performance logging, and anomaly detection
• Build model testing suites: unit tests, integration tests, and validation against held-out datasets; implement data drift detection and model decay alerting

Continuous Learning

• Maintain active, current knowledge of ML frameworks, tooling, and research — testing new approaches and bringing relevant innovations into the team’s engineering practice
• Contribute to internal knowledge sharing: documenting patterns, building reusable components, and supporting capability development within the team

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 team builds some of the most technically advanced AI systems in enterprise technology today. For an engineer at the start of their career in AI, there is no better environment to learn fast, work on hard problems, and build the depth and breadth that will define the next decade of enterprise AI. The work ships. The feedback loop is tight. The team is exceptional.

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: Hands-On Builder | ML Engineering | Production Excellence | Continuous Learner

Core Requirements

• 3+ years of software engineering in production — Python, data engineering, or analytics engineering
• Hands-on experience building and deploying machine learning models in production environments
• Strong understanding of ML fundamentals: supervised and unsupervised learning, feature engineering, model evaluation
• Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn, or XGBoost)
• 2+ years building data pipelines or ETL workflows
• Strong SQL and data manipulation skills
• Experience with MLOps practices: model versioning, experiment tracking, CI/CD for ML
• Familiarity with cloud platforms (AWS, Azure, or GCP) and containerised workloads

Additional Strong Signals

• Experience with advanced ML techniques: deep learning, NLP, computer vision, or time series forecasting
• Production experience with model serving frameworks (BentoML, KServe, Seldon, or FastAPI)
• Exposure to experiment tracking tools (MLflow, Weights & Biases, or equivalent)
• Experience with data quality and validation frameworks
• Hands-on experience with classical software engineering: version control, code review, testing, and CI/CD
• Cloud-native development: Kubernetes, managed services, infrastructure as code
• Experience translating business requirements into ML solutions
• Active engagement with the AI/ML community: open source contributions, side projects, or published learning
• Bachelor’s degree in Computer Science, Engineering, or a related field
• Proficiency with AI-assisted development tools (Claude Code, GitHub Copilot)

AI ENGINEER – MANAGER

AI Engineer – Manager
Data & AI | Accenture

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 (Manager) leads the technical delivery of AI programmes while remaining an active, hands-on engineer. This role bridges the gap between business problems and technical solutions — working directly with clients to understand requirements, translating them into AI solution designs, and leading a team of engineers to deliver production-grade systems that generate measurable value.

Managers on this team operate as Forward Deployed Engineers — brought into client environments to rapidly understand a business problem, design an AI solution, and build it. The expectation is genuine technical depth combined with client credibility: the Manager must be as effective in a code review or design session as they are in a client workshop. They develop their team, grow client relationships, and continuously evolve their knowledge of AI as the field advances.

Position Responsibilities

Technical Solution Design and Hands-On Delivery

• Architect and deliver AI solutions end-to-end — machine learning systems, data pipelines, model serving, and AI-powered applications — at production engineering standards, contributing directly to code and design when required
• Design model harnesses, training pipelines, serving architectures, and integration patterns for diverse use cases
• Build and maintain data pipelines, implement advanced knowledge retrieval components, and design robust model lifecycle management
• Establish ML architecture standards — model versioning, experimentation methodology, evaluation frameworks, and feature engineering patterns
• Lead the design and implementation of MLOps practices: CI/CD for ML, automated testing, model registry management, and deployment strategies

AI Technical Delivery

• Architect and build production AI systems hands-on — model training, serving, monitoring, and integration; write code, resolve complex engineering problems, and set the quality standard through personal example
• Design and implement end-to-end ML pipelines: data preparation, feature engineering, model training, evaluation, serving, and continuous monitoring
• Build and operate evaluation and MLOps pipelines: benchmark datasets, model performance metrics, A/B testing frameworks, ML testing suites (unit, integration, simulation), and production observability
• Implement trust, safety, and governance components: model explainability, bias detection and mitigation, data privacy safeguards, and audit trail design for enterprise compliance

Client Engagement and Business Translation

• Serve as the primary technical point of contact for client stakeholders — running workshops, translating business requirements into AI solution designs, and communicating technical decisions clearly to non-technical audiences
• Develop initial value hypotheses for AI solutions — identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery begins
• Contribute to solution design and proposal development; identify expansion opportunities within current engagements and support account growth

Delivery Excellence and MLOps

• Lead workstream delivery in agile environments — managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibility
• Establish MLOps and DevOps practices: CI/CD for model code and training scripts, automated evaluation gates, deployment strategies, model and asset registry management, and production operations
• Define and implement evaluation frameworks, model testing suites, HITL feedback capture, and production observability — performance tracking, cost monitoring, latency profiling, and drift detection
• Implement guardrails, fairness checks, bias mitigation, and audit trail design for enterprise compliance and responsible AI

Team Leadership and Development

• Lead, manage, and develop a team of Consultants and Analysts — setting clear expectations, providing technical coaching, and running structured code reviews and design sessions
• Foster a delivery culture of engineering rigour, continuous improvement, and learning — supporting team members in building AI depth through challenging work and active knowledge sharing

Innovation and Continuous Learning

• Maintain current, hands-on knowledge of AI developments — testing new frameworks, tooling, and research; bringing relevant advances into the team’s engineering practice
• Contribute to internal practice development: reusable accelerators, reference implementations, and delivery standards that improve capability across Accenture’s AI practice

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 engineers ready to lead — technically and with clients. You will own the full arc of AI delivery: from understanding the business problem to shipping production systems that demonstrably move the needle. The team operates at the frontier, the clients are serious, and the work has real consequences. If you are technically excellent, client-credible, and driven to build things that matter, this is the right next step.

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: Hands-On Technical Lead | AI Delivery | Client Engagement | Team Leadership

Core Requirements

• 3+ years building AI/ML systems in production — with operational accountability for deployed models
• Experience facilitating client workshops to elicit requirements and design AI solutions
• 2+ years designing and delivering ML systems — models operating with meaningful value delivery in real production environments
• Experience developing value hypotheses and business cases for AI initiatives
• Hands-on experience with ML orchestration and MLOps (model versioning, experiment tracking, CI/CD for ML)
• Demonstrated experience across the ML Development Lifecycle: problem definition, data preparation, model training, evaluation, serving, monitoring, and refinement
• Experience with model evaluation frameworks, A/B testing, and performance metrics in production
• Proven track record deploying ML systems in production with measurable results — reliability, performance, or business value outcomes
• Experience with guardrails, bias detection, fairness assessment, and compliance-focused audit trails
• Experience with advanced ML techniques: deep learning, NLP, computer vision, or time series forecasting
• 5+ years experience in classical AI/ML, data engineering, or advanced analytics — integrating intelligent systems into production software
• 6+ years full stack engineering: Python and data engineering; active, hands-on capability across the stack
• 6+ years cloud-native development on AWS, Azure, or GCP — CI/CD, containerised workloads, infrastructure as code, and production operations
• 3+ years technical leadership: design ownership, code review, and engineer mentoring

Additional Strong Signals

• Experience integrating AI systems with enterprise platforms (SAP, Salesforce, ServiceNow) via APIs
• Experience on complex digital transformation programmes — enterprise-scale, multi-workstream, client-facing delivery
• Contributions to internal accelerators, reusable frameworks, or thought leadership in AI
• Experience engaging directly with C-suite stakeholders — translating business requirements to technical solutions
• Bachelor’s degree in a related field. A Master’s degree is highly valued.

AI ENGINEER – ASSOCIATE DIRECTOR

AI Engineer – Associate Director
Data & AI | Accenture

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

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.
Core Requirements

• 5+ 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
• 5+ 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.

About Accenture

Visit us at www.accenture.com

Equal Employment Opportunity Statement

We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.

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