{"id":3901558,"url":"https://alion.io/job/accenture-ai-engineer-associate-director","title":"AI Engineer – Associate Director","company":{"id":271,"name":"Accenture","domain":"accenture.com","url":"https://alion.io/company/accenture","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":2116,"ghost_share":0.001,"stale_share":0.17,"repost_share":0.006,"time_to_fill_p50_days":5,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"head","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":14,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BentoML","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Computer Vision","optional":false},{"name":"Copilot","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"KServe","optional":false},{"name":"Kubernetes","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Seldon Core","optional":false},{"name":"ServiceNow","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"Weights & Biases","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-11-06T00:00:00Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-08T08:28:44Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"AI ENGINEER - CONSULTANT\nAI Engineer - Consultant\nData & AI | Accenture\nAbout Accenture Data & AI\nThe 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.\nWith 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.\nAbout the Role\nThe 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.\nThe 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.\nPosition Responsibilities\nAI Application Development\nDesign and build AI-powered applications - Python backends, REST and event-driven APIs, and frontend interfaces - to production engineering standards\nImplement AI application workflows: model serving, prediction pipelines, real-time inference, and integration with business systems\nTranslate business requirements into technical specifications; work with managers and clients to clarify scope, surface ambiguities, and deliver against agreed outcomes\nMachine Learning Development\nBuild, train, and deploy machine learning models using established frameworks (PyTorch, TensorFlow, scikit-learn, LightGBM): feature engineering, model selection, hyperparameter tuning, and lifecycle management\nImplement appropriate ML techniques for different use cases: supervised learning, unsupervised learning, time series forecasting, classification, and regression\nDesign and maintain model versioning, experimentation tracking, and reproducible training pipelines using MLOps tools\nData Engineering & Pipelines\nDesign and implement data pipelines: ingestion, transformation, enrichment, and preparation at scale\nBuild ETL/ELT workflows connecting data sources to ML training and inference systems\nImplement data quality checks, validation, and monitoring to ensure data integrity throughout the pipeline\nModel & System Integration\nIntegrate trained models into production systems with robust error handling, monitoring, and rollback capabilities\nDesign and implement model serving architectures: batch prediction, real-time APIs, and edge deployment where required\nBuild ML monitoring and observability: prediction drift detection, model performance tracking, and automated retraining triggers\nMLOps & Production Excellence\nMaintain CI/CD pipelines for model code, training scripts, and infrastructure - including automated testing and deployment strategies\nInstrument ML systems with production observability: latency tracking, prediction profiling, model performance logging, and anomaly detection\nBuild model testing suites: unit tests, integration tests, and validation against held-out datasets; implement data drift detection and model decay alerting\nContinuous Learning\nMaintain active, current knowledge of ML frameworks, tooling, and research - testing new approaches and bringing relevant innovations into the team’s engineering practice\nContribute to internal knowledge sharing: documenting patterns, building reusable components, and supporting capability development within the team\nTravel may be required for this role. The extent of travel will vary from 0% to 100% depending on client requirements and business need.\nWhy This Role\nThis 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.\nAccenture 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.\nCore Pillars: Hands-On Builder | ML Engineering | Production Excellence | Continuous Learner\nCore Requirements\n3+ years of software engineering in production - Python, data engineering, or analytics engineering\nHands-on experience building and deploying machine learning models in production environments\nStrong understanding of ML fundamentals: supervised and unsupervised learning, feature engineering, model evaluation\nExperience with ML frameworks (PyTorch, TensorFlow, scikit-learn, or XGBoost)\n2+ years building data pipelines or ETL workflows\nStrong SQL and data manipulation skills\nExperience with MLOps practices: model versioning, experiment tracking, CI/CD for ML\nFamiliarity with cloud platforms (AWS, Azure, or GCP) and containerised workloads\nAdditional Strong Signals\nExperience with advanced ML techniques: deep learning, NLP, computer vision, or time series forecasting\nProduction experience with model serving frameworks (BentoML, KServe, Seldon, or FastAPI)\nExposure to experiment tracking tools (MLflow, Weights & Biases, or equivalent)\nExperience with data quality and validation frameworks\nHands-on experience with classical software engineering: version control, code review, testing, and CI/CD\nCloud-native development: Kubernetes, managed services, infrastructure as code\nExperience translating business requirements into ML solutions\nActive engagement with the AI/ML community: open source contributions, side projects, or published learning\nBachelor’s degree in Computer Science, Engineering, or a related field\nProficiency with AI-assisted development tools (Claude Code, GitHub Copilot)\nAI ENGINEER - MANAGER\nAI Engineer - Manager\nData & AI | Accenture\nAbout Accenture Data & AI\nThe 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.\nWith 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.\nAbout the Role\nThe 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.\nManagers 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.\nPosition Responsibilities\nTechnical Solution Design and Hands-On Delivery\nArchitect 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\nDesign model harnesses, training pipelines, serving architectures, and integration patterns for diverse use cases\nBuild and maintain data pipelines, implement advanced knowledge retrieval components, and design robust model lifecycle management\nEstablish ML architecture standards - model versioning, experimentation methodology, evaluation frameworks, and feature engineering patterns\nLead the design and implementation of MLOps practices: CI/CD for ML, automated testing, model registry management, and deployment strategies\nAI Technical Delivery\nArchitect 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\nDesign and implement end-to-end ML pipelines: data preparation, feature engineering, model training, evaluation, serving, and continuous monitoring\nBuild and operate evaluation and MLOps pipelines: benchmark datasets, model performance metrics, A/B testing frameworks, ML testing suites (unit, integration, simulation), and production observability\nImplement trust, safety, and governance components: model explainability, bias detection and mitigation, data privacy safeguards, and audit trail design for enterprise compliance\nClient Engagement and Business Translation\nServe 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\nDevelop initial value hypotheses for AI solutions - identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery begins\nContribute to solution design and proposal development; identify expansion opportunities within current engagements and support account growth\nDelivery Excellence and MLOps\nLead workstream delivery in agile environments - managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibility\nEstablish 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\nDefine and implement evaluation frameworks, model testing suites, HITL feedback capture, and production observability - performance tracking, cost monitoring, latency profiling, and drift detection\nImplement guardrails, fairness checks, bias mitigation, and audit trail design for enterprise compliance and responsible AI\nTeam Leadership and Development\nLead, manage, and develop a team of Consultants and Analysts - setting clear expectations, providing technical coaching, and running structured code reviews and design sessions\nFoster a delivery culture of engineering rigour, continuous improvement, and learning - supporting team members in building AI depth through challenging work and active knowledge sharing\nInnovation and Continuous Learning\nMaintain current, hands-on knowledge of AI developments - testing new frameworks, tooling, and research; bringing relevant advances into the team’s engineering practice\nContribute to internal practice development: reusable accelerators, reference implementations, and delivery standards that improve capability across Accenture’s AI practice\nTravel may be required for this role. The extent of travel will vary from 0% to 100% depending on client requirements and business need.\nWhy This Role\nThis role is for engineers ready to lead - technically and with clients. 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