I&P - GN - SONG - AI & Data - Platforms - AI/ML Engineering - Consultant

About Song - Data & AI
Accenture Song uses AI, proprietary customer data, and product platforms to help clients improve customer experience and drive measurable growth across marketing, sales, commerce, and service. From strategy through execution, Song Data & AI helps organizations build and operationalize advanced capabilities - covering customer data unification, predictive analytics, and Generative AI (including agentic use cases) - to enable smarter decisioning like personalization and “next best action,” faster content and experience delivery, and more effective commerce and customer engagement.
What's In It For You?
Join a worldwide network of data scientists, ML/AI engineers, and lifecycle engineering practitioners building practical, trusted, and production-grade AI solutions.
Access world-class training, mentorship, and certifications across machine learning, deep learning, Generative AI, Agentic AI, MLOps, LLMOps, AgentOps, and cloud AI platforms.
Work on high-visibility engagements across Marketing, Sales, Commerce, Customer Service, and Digital Products - taking assigned AI workstreams from experimentation through deployment and monitoring.
Contribute to Accenture's reusable model and agent components, evaluation frameworks, engineering standards, and lifecycle playbooks.
What You Will Do
As an ML/AI Engineering Consultant, you will own the hands-on design, development, testing, deployment, and monitoring of assigned machine learning and AI workstreams. You will combine model development with production engineering and lifecycle practices, while guiding junior team members and collaborating with cross-functional client teams. Translate defined business problems and user needs into data requirements, ML/AI problem statements, experiments, success metrics, detailed designs, and executable technical tasks.
Perform data exploration, feature engineering, model selection, training, tuning, evaluation, explainability, error analysis, and champion-challenger comparisons.
Build predictive, forecasting, recommendation, optimization, NLP, computer vision, and other fit-for-purpose machine learning solutions.
Develop foundation-model applications using prompt engineering, embeddings, vector search, Retrieval-Augmented Generation, structured outputs, evaluation, and guardrails.
Implement agentic workflows using tool and function calling, planning, memory and context, orchestration, multi-agent patterns, and human-in-the-loop controls.
Engineer modular, production-quality Python components, APIs and services, batch or real-time inference workflows, containers, and enterprise integrations.
Implement AI lifecycle pipelines covering experiment tracking, data and model versioning, prompt or configuration versioning, registries, CI/CD, continuous training, promotion, rollback, and retraining.
Configure evaluation, monitoring, and observability for model quality, drift, robustness, safety, latency, reliability, cost, adoption, and business KPIs; troubleshoot production issues.
Apply Responsible AI, security, privacy, testing, reproducibility, auditability, and human oversight requirements throughout development and operations.
Collaborate with data engineers, software and cloud engineers, architects, UX teams, domain specialists, and product owners to deliver end-to-end solutions.
Own an assigned module or workstream, review code and model artefacts, coordinate Analysts or Senior Analysts, and manage day-to-day technical dependencies and risks.
Participate in client workshops, design walkthroughs, demonstrations, technical documentation, and contribution to reusable accelerators and practice assets.
Domain Focus
Candidates should bring hands-on ML/AI solution-building and productionization experience in one or more of the following domains: Marketing - customer propensity, segmentation, personalization, next-best action, content intelligence, media effectiveness, and campaign optimization
Sales - demand forecasting, recommendations, revenue intelligence, sales productivity, outlet or customer prioritization, and decision support
Commerce - search, recommendations, pricing, promotion, demand and inventory intelligence, and digital commerce optimization
Service - conversational AI, contact center intelligence, knowledge assistance, case routing, quality monitoring, and operations automation
Design & Digital Products - AI-powered product features, product intelligence, experimentation, personalization, and agile digital product delivery
Who We Are Looking For
Mandatory
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related discipline.
4-8 years of progressive experience in machine learning, AI engineering, data science, and/or software engineering, including ownership of production AI workstreams.
Strong hands-on expertise in Python and SQL, together with production-quality software engineering, version control, testing, modular design, and API or service development.
Strong foundations in supervised and unsupervised learning, statistics, feature engineering, model evaluation, experimentation, and selection of fit-for-purpose techniques.
Hands-on experience with common ML and deep learning libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent.
Demonstrated experience taking at least one ML/AI solution from experimentation into production deployment, monitoring, and ongoing support.
Hands-on Generative AI experience with prompt engineering, embeddings, vector search, Retrieval-Augmented Generation, structured outputs, evaluation, and guardrails.
Working experience with agentic AI patterns such as tool and function calling, planning, memory or context, orchestration, and multi-agent workflows using LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent.
Experience with MLOps, LLMOps, and/or AgentOps practices, including experiment tracking, versioning, model registries, CI/CD, promotion, rollback, retraining, and lifecycle monitoring.
Solid experience with at least one major cloud or AI platform - AWS, Azure, GCP, Databricks, Snowflake, or Anthropic - and its relevant AI services.
Experience deploying AI through APIs, containers, Kubernetes or managed services, and batch or real-time inference patterns; familiarity with FastAPI, Docker, or equivalent.
Working knowledge of data platforms and pipelines, relational and vector databases, unstructured data, and API-based integrations.
Experience implementing evaluation and monitoring across accuracy, drift, robustness, safety, latency, reliability, cost, adoption, and business outcomes.
Awareness of Responsible AI, security, privacy, observability, reproducibility, auditability, testing, and human oversight required for enterprise production.
Strong analytical, problem-solving, stakeholder engagement, and communication skills, with the ability to explain technical findings to business and technical audiences.
Experience leading a technical module or small workstream, estimating tasks, reviewing code and model artefacts, managing dependencies, and mentoring junior team members.
Good to Have
Master's degree, PhD, recognized cloud or AI certifications, or relevant consulting experience.
Depth in one or more advanced areas such as time series, recommender systems, optimization, causal inference, probabilistic modelling, NLP, or computer vision.
Experience with Kubernetes, model serving, feature stores, vector databases, distributed training, or scalable batch and real-time inference.
Exposure to GPU-based training or inference, model compression, performance engineering, or NVIDIA technologies.
Experience with AI governance platforms, model risk management, red teaming, adversarial testing, or regulatory compliance.
Domain experience in Marketing, Sales, Commerce, Service, or Digital Products, and exposure to Agile or client-facing consulting delivery.
What We Are NOT Looking For Pure Data Scientists or researchers without production engineering, deployment, monitoring, or lifecycle ownership.
Prompt-engineering-only or LLM-only profiles without strong foundations in statistics, machine learning, and rigorous evaluation.
MLOps, DevOps, or infrastructure-only profiles without substantive model development, experimentation, and evaluation capability.
Notebook-only profiles without production-quality software engineering, API or service development, testing, and deployment experience.
Delivery-management-only, presales-only, or coordination-only profiles without hands-on AI solution development and technical workstream ownership.
About Accenture Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.
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