AI Engineer – Associate Director

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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-perform

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