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.

Key Responsibilities

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.

Requirements & Tech Stack

• Python
• Generative AI
• Machine Learning
• Deep Learning
• NLP
• Computer Vision
• MLOps
• LLMOps
• AgentOps
• Cloud AI Platforms
• Prompt Engineering
• Embeddings
• Vector Search
• Retrieval-Augmented Generation
• Tool and Function Calling
• CI/CD
• Responsible AI

Qualifications

• Hands-on design, development, testing, deployment, and monitoring of ML/AI workstreams.
• Experience combining model development with production engineering and lifecycle practices.
• Ability to guide junior team members.
• Collaboration with cross-functional client teams.
• Experience translating business problems into data requirements and ML/AI problem statements.
• Proficiency in data exploration, feature engineering, model selection, training, tuning, and evaluation.
• Experience with foundation-model applications.
• Familiarity with agentic workflows.
• Experience engineering production-quality Python components, APIs, and services.
• Knowledge of AI lifecycle pipelines.
• Experience with evaluation, monitoring, and observability for AI models.
• Understanding of Responsible AI, security, privacy, testing, reproducibility, auditability, and human oversight.
• Experience collaborating with various engineering and client teams.
• Ability to own modules/workstreams, review code, and manage technical dependencies and risks.
• Experience participating in client workshops and contributing to practice assets.