AI Engineer

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)