Senior AI Engineer

About Accenture Data & AIThe 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 RoleThe 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 ResponsibilitiesTechnical Solution Design and Hands-On DeliveryArchitect 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 requiredDesign model harnesses, training pipelines, serving architectures, and integration patterns for diverse use casesBuild and maintain data pipelines, implement advanced knowledge retrieval components, and design robust model lifecycle managementEstablish ML architecture standards — model versioning, experimentation methodology, evaluation frameworks, and feature engineering patternsLead the design and implementation of MLOps practices: CI/CD for ML, automated testing, model registry management, and deployment strategiesAI Technical DeliveryArchitect 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 exampleDesign and implement end-to-end ML pipelines: data preparation, feature engineering, model training, evaluation, serving, and continuous monitoringBuild and operate evaluation and MLOps pipelines: benchmark datasets, model performance metrics, A/B testing frameworks, ML testing suites (unit, integration, simulation), and production observabilityImplement trust, safety, and governance components: model explainability, bias detection and mitigation, data privacy safeguards, and audit trail design for enterprise complianceClient Engagement and Business TranslationServe 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 audiencesDevelop initial value hypotheses for AI solutions — identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery beginsContribute to solution design and proposal development; identify expansion opportunities within current engagements and support account growthDelivery Excellence and MLOpsLead workstream delivery in agile environments — managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibilityEstablish MLOps and DevOps practices: CI/CD for model code and training scripts, automated evaluation gates, deployment strategies, model and asset registry management, and production operationsDefine and implement evaluation frameworks, model testing suites, HITL feedback capture, and production observability — performance tracking, cost monitoring, latency profiling, and drift detectionImplement guardrails, fairness checks, bias mitigation, and audit trail design for enterprise compliance and responsible AITeam Leadership and DevelopmentLead, manage, and develop a team of Consultants and Analysts — setting clear expectations, providing technical coaching, and running structured code reviews and design sessionsFoster a delivery culture of engineering rigour, continuous improvement, and learning — supporting team members in building AI depth through challenging work and active knowledge sharingInnovation and Continuous LearningMaintain current, hands-on knowledge of AI developments — testing new frameworks, tooling, and research; bringing relevant advances into the team’s engineering practiceContribute to internal practice development: reusable accelerators, reference implementations, and delivery standards that improve capability across Accenture’s AI practiceTravel 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 RoleThis 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 LeadershipCore Requirements3+ years building AI/ML systems in production — with operational accountability for deployed modelsExperience facilitating client workshops to elicit requirements and design AI solutions2+ years designing and delivering ML systems — models operating with meaningful value delivery in real production environmentsExperience developing value hypotheses and business cases for AI initiativesHands-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 refinementExperience with model evaluation frameworks, A/B testing, and performance metrics in productionProven track record deploying ML systems in production with measurable results — reliability, performance, or business value outcomesExperience with guardrails, bias detection, fairness assessment, and compliance-focused audit trailsExperience with advanced ML techniques: deep learning, NLP, computer vision, or time series forecasting5+ years experience in classical AI/ML, data engineering, or advanced analytics — integrating intelligent systems into production software6+ years full stack engineering: Python and data engineering; active, hands-on capability across the stack6+ years cloud-native development on AWS, Azure, or GCP — CI/CD, containerised workloads, infrastructure as code, and production operations3+ years technical leadership: design ownership, code review, and engineer mentoringAdditional Strong SignalsExperience integrating AI systems with enterprise platforms (SAP, Salesforce, ServiceNow) via APIsExperience on complex digital transformation programmes — enterprise-scale, multi-workstream, client-facing deliveryContributions to internal accelerators, reusable frameworks, or thought leadership in AIExperience engaging directly with C-suite stakeholders — translating business requirements to technical solutionsBachelor’s degree in a related field. A Master’s degree is highly valued.