AI Engineer

About Accenture Data & AI

A new Data & AI decade—set to transform work and society—is already underway. Accenture is moving into this future with a clear 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.

With more than 45,000 Data & AI professionals, Accenture’s Data & AI organisation brings together Experienced Innovation, Strategic Investment, Exceptional Talent, and a Power Ecosystem to deliver outcomes at the cutting edge of what’s possible.

About the Role

The AI Engineer (Consultant) is a practical engineer at the centre of Accenture’s AI delivery capability. This role is for engineers who build intelligent systems—machine learning pipelines, AI-powered applications, data-driven solutions, and AI integrations—and who want to deliver this work at the forefront of enterprise AI. Consultants collaborate within delivery teams, transforming business requirements into production-ready AI solutions, guided by technical leads and managers.

You will be expected to engineer actively every day: writing code, developing machine learning models, creating AI pipelines, implementing integrations, and sustaining MLOps and DevOps practices that keep production systems running. Consultants in this team must also commit to ongoing learning; as AI evolves quickly, staying current is a professional responsibility, not an optional activity.

Position Responsibilities

AI Application Development

• Design and develop AI-powered applications—Python backends, REST and event-driven APIs, and frontend interfaces—meeting production engineering standards
• Implement AI application workflows, including model serving, prediction pipelines, real-time inference, and integration with business systems
• Convert business requirements into technical specifications; partner with managers and clients to clarify scope, identify ambiguities, and deliver against agreed outcomes

Machine Learning Development

• Build, train, and deploy machine learning models using recognised frameworks (PyTorch, TensorFlow, scikit-learn, LightGBM), including feature engineering, model selection, hyperparameter tuning, and lifecycle management
• Apply suitable ML approaches for each use case: supervised learning, unsupervised learning, time series forecasting, classification, and regression
• Create and maintain model versioning, experimentation tracking, and reproducible training pipelines using MLOps tools

Data Engineering & Pipelines

• Design and implement scalable data pipelines for ingestion, transformation, enrichment, and preparation
• Build ETL/ELT workflows that connect data sources to ML training and inference systems
• Implement data quality controls, validation, and monitoring to preserve data integrity throughout the pipeline

Model & System Integration

• Deploy trained models into production systems with strong error handling, monitoring, and rollback capabilities
• Design and implement model serving architectures, including batch prediction, real-time APIs, and edge deployment when needed
• Develop ML monitoring and observability capabilities, such as 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 approaches
• Enable production observability for ML systems: latency tracking, prediction profiling, model performance logging, and anomaly detection
• Create model testing suites, including unit tests, integration tests, and validation against held-out datasets; implement data drift detection and model decay alerting

Continuous Learning

• Stay actively current with ML frameworks, tooling, and research—evaluate new approaches and introduce relevant innovations into the team’s engineering practice
• Support internal knowledge sharing by documenting patterns, building reusable components, and helping grow team capabilities

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 delivers some of the most technically advanced AI systems in enterprise technology today. For an AI engineer early in their career, there is no better setting to accelerate learning, tackle difficult challenges, and build the depth and breadth that will shape the next decade of enterprise AI. The work ships, the feedback cycle is fast, and 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 experience in production—Python, data engineering, or analytics engineering
• Hands-on experience building and deploying machine learning models in production environments
• Strong command of ML fundamentals: supervised and unsupervised learning, feature engineering, and 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
• MLOps experience, including model versioning, experiment tracking, and CI/CD for ML
• Familiarity with cloud platforms (AWS, Azure, or GCP) and containerised workloads

Additional Strong Signals

• Experience applying advanced ML methods: deep learning, NLP, computer vision, or time series forecasting
• Production experience with model serving frameworks (BentoML, KServe, Seldon, or FastAPI)
• Experience with experiment tracking tools (MLflow, Weights & Biases, or an equivalent platform)
• Experience with data quality and validation frameworks
• Practical experience in classical software engineering: version control, code review, testing, and CI/CD
• Cloud-native development skills: Kubernetes, managed services, infrastructure as code
• Experience translating business requirements into ML solutions
• Active participation in the AI/ML community, such as open source contributions, side projects, or published learning
• Bachelor's degree in Computer Science, Engineering, or a related discipline
• Proficiency with AI-assisted development tools (Claude Code, GitHub Copilot)