Senior AI Engineer

About Accenture Data & AI

A new Data & AI decade is beginning—one that will reshape work and society. Accenture is moving forward with a focused strategy and purpose: helping clients optimise and reinvent their businesses with data and AI, supported by a $3 billion investment and a commitment to industry-defining work.

Accenture’s Data & AI organisation brings together Experienced Innovation, Strategic Investment, Exceptional Talent, and a Power Ecosystem to deliver outcomes at the leading edge of what’s possible. With more than 45,000 professionals dedicated to Data & AI, the organisation is built to turn cutting-edge ideas into real business impact.

About the Role

The AI Engineer (Manager) is responsible for leading the technical delivery of AI programmes while staying actively hands-on. This role connects business challenges with technical execution—working with clients to understand requirements, converting them into AI solution designs, and leading a team of engineers to build production-ready systems that deliver measurable value.

Managers in this team work as Forward Deployed Engineers—embedded within client environments to quickly understand a business problem, design an AI solution, and build it. The role requires authentic technical depth and strong client credibility. The Manager must be equally effective in code reviews and design discussions as they are in client workshops. They also develop their team, strengthen client relationships, and keep expanding their AI expertise as the field evolves.

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—meeting production engineering standards, contributing directly to code and design when needed
• Design model harnesses, training pipelines, serving architectures, and integration patterns for a range of use cases
• Build and maintain data pipelines, implement advanced knowledge retrieval components, and design dependable model lifecycle management
• Set ML architecture standards, including 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, solve complex engineering challenges, and establish quality standards 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 capabilities: model explainability, bias detection and mitigation, data privacy safeguards, and audit trail design for enterprise compliance

Client Engagement and Business Translation

• Act as the primary technical contact for client stakeholders—facilitating workshops, translating business requirements into AI solution designs, and clearly communicating technical decisions to non-technical audiences
• Create initial value hypotheses for AI solutions by identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery begins
• Support solution design and proposal development by identifying expansion opportunities within existing engagements and contributing to account growth

Delivery Excellence and MLOps

• Lead delivery of workstreams in agile environments—owning 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—track performance, monitor costs, profile latency, and detect drift
• Implement guardrails, fairness checks, bias mitigation, and audit trail design to meet enterprise compliance expectations and support responsible AI

Team Leadership and Development

• Lead, manage, and develop a team of Consultants and Analysts—set clear expectations, provide technical coaching, and run structured code reviews and design sessions
• Build a delivery culture centered on engineering rigour, continuous improvement, and learning—helping team members deepen AI expertise through challenging work and active knowledge sharing

Innovation and Continuous Learning

• Keep current with hands-on AI developments—test new frameworks, tooling, and research, and bring relevant advances into the team’s engineering practice
• Contribute to internal practice development, including reusable accelerators, reference implementations, and delivery standards that strengthen capability across Accenture’s AI practice

Travel may be required for this role. Travel levels may range from 0% to 100% based on client requirements and business need.

Why This Role

This role is designed for engineers who are ready to lead—both technically and with clients. You will be accountable for the complete AI delivery lifecycle, from understanding the business problem to delivering production systems that demonstrably improve outcomes. The team works at the frontier, clients are highly engaged, and the work has meaningful impact. If you are technically strong, client-ready, and motivated to build solutions 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 gather requirements and design AI solutions
• 2+ years designing and delivering ML systems—models delivering meaningful value 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)
• Proven 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 production performance metrics
• Demonstrated track record deploying ML systems in production with measurable outcomes—reliability, performance, or business value results
• 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 mentoring engineers

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 into technical solutions
• Bachelor's degree in a related field. A Master's degree is highly valued.