AI Engineer – Senior Manager
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
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.
Core Requirements
- 5+ years building AI/ML systems in production — with operational accountability for deployed models
- Experience facilitating client workshops to elicit requirements and design AI solutions
- 4+ 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
- 7+ years experience in classical AI/ML, data engineering, or advanced analytics — integrating intelligent systems into production software
- 8+ years full stack engineering: Python and data engineering; active, hands-on capability across the stack
- 8+ 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.
Singapore
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