Senior AI Engineer in Hampshire

About Accenture Data 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.