#ACN I&P - GN - SONG - AI & Data - Platforms - AI/ML Engineering - Manager

Accenture in India; Bengaluru; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Accenture Song seeks an ML/AI Engineering Manager to lead the design, development, and deployment of machine learning, Generative AI, and agentic solutions across multiple domains. You will align business goals with AI strategies, drive experimentation, and scale production-grade AI systems.

You will guide feature engineering, model selection, and lifecycle governance while collaborating with senior client stakeholders to implement responsible AI, security, and governance across engagements.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or related field.
• 8-12 years of progressive ML/AI engineering, data science, or digital consulting with leadership experience.
• Strong expertise in Python and SQL, production-quality software engineering, version control, testing, and API/service development.
• Hands-on experience with ML libraries (scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch) and generative AI capabilities.
• Experience with MLOps, LLMOps, AgentOps, experiment tracking, versioning, registries, CI/CD, and lifecycle governance.
• Proven ability to translate business needs into AI solutions and to lead enterprise deployments.

Responsibilities

• Translate business decisions into AI/ML problem statements, experimentation plans, and delivery roadmaps.
• Architect and lead development of predictive, forecasting, NLP, vision, and generative AI solutions.
• Guide feature engineering, model selection, training, evaluation, and governance.
• Design foundation-model apps using prompt engineering, embeddings, and Retrieval-Augmented Generation.
• Architect agentic systems with tool calling, planning, memory, and multi-agent orchestration.
• Define AI lifecycle pipelines, versioning, CI/CD, and end-of-life controls.
• Lead production engineering for modular APIs, containers, and real-time inference.
• Embed Responsible AI, security, privacy, testing, and governance into development.
• Engage with senior client stakeholders to shape AI strategy and value realization.
• Manage delivery governance, risk, quality, and cross-functional collaboration.
• Mentor analytics teams and contribute to internal accelerators and playbooks.

Skills

Python

SQL

Production-grade software

ML/AI leadership

MLOps

Cloud platforms

Stakeholder management

Kubernetes

Education

Bachelor's/Master in CS/Math/DS/Engineering

Tools

LangChain

LlamaIndex

LangGraph

CrewAI

Job description

Entity: GN Song

Practice: GN Song - Data & AI

Title: Song Process Excellence | ML/AI Engineering Manager - CL7

Job Location: Gurgaon/ Bangalore/ Mumbai/ Hyderabad/ Pune/ Kolkata/ Chennai

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 AI solutions from experimentation through enterprise deployment and continuous improvement.
• Contribute to Accenture's internal accelerators, reusable model and agent components, evaluation frameworks, engineering standards, and lifecycle playbooks.

What You Will Do

As an ML/AI Engineering Manager, you will lead the design, development, deployment, and continuous improvement of machine learning, Generative AI, and agentic solutions, combining computational science, software engineering, lifecycle engineering, and consulting leadership.

• Translate business decisions and user needs into AI/ML problem statements, experimentation plans, solution architectures, success metrics, and prioritized delivery roadmaps.
• Architect and lead development of predictive, forecasting, recommendation, optimization, NLP, computer vision, Generative AI, and agentic solutions aligned to the problem context.
• Guide feature engineering, algorithm and model selection, training, tuning, experimentation, evaluation, explainability, error analysis, and champion‑challenger decisions.
• Design foundation‑model applications using prompt engineering, embeddings, vector search, Retrieval‑Augmented Generation, structured outputs, model adaptation, evaluation, and guardrails.
• Architect agentic systems using tool and function calling, planning, memory and context, orchestration, multi‑agent patterns, and human‑in‑the‑loop controls.
• Define AI lifecycle pipelines covering experiment tracking, data and model versioning, registries, CI/CD and continuous training, promotion, rollback, retraining, and end‑of‑life controls.
• Lead production engineering for modular APIs and services, batch and real‑time inference, containers, scalable compute, enterprise integration, and resilient deployment patterns.
• Implement monitoring and observability for model quality, drift, safety, latency, reliability, cost, adoption, and business outcomes, with governed learning and improvement loops.
• Embed Responsible AI, security, privacy, testing, reproducibility, auditability, and human oversight into model and agent development and operations.
• Engage with senior client stakeholders to shape AI strategies, platform roadmaps, operating models, business cases, solution demonstrations, and value realization plans.
• Manage delivery governance, resource planning, risk, quality, project economics, and partner coordination across multi‑disciplinary AI engagements.
• Mentor Consultants and Analysts, and shape internal accelerators, reusable components, evaluation assets, engineering standards, and playbooks across the practice.

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. Advanced degree or MBA from a top institution is advantageous.
• 8-12 years of progressive experience in machine learning, AI engineering, data science, and/or digital consulting, with significant consulting or enterprise leadership experience.
• Strong expertise in Python and SQL, production‑quality software engineering, version control, testing, and modular 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 success leading ML/AI solutions from problem framing and experimentation through enterprise deployment, adoption, monitoring, and continuous improvement.
• Hands‑on Generative AI experience with prompt engineering, embeddings, vector search, Retrieval‑Augmented Generation, structured outputs, evaluation, guardrails, and model adaptation.
• Agentic AI experience with tool and function calling, planning, memory and context, orchestration, and multi‑agent patterns using LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent.
• Experience with MLOps, LLMOps, and/or AgentOps practices, including experiment tracking, versioning, model registries, CI/CD, continuous training, promotion, rollback, retraining, and lifecycle governance.
• Solid experience with at least one major cloud or AI platform - AWS, Azure, GCP, Databricks, Snowflake, or Anthropic - and its relevant services for AI development and deployment.
• 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; familiarity with Spark or Databricks, relational and vector databases, unstructured data, and API‑based integrations.
• Experience defining evaluation and monitoring across accuracy, drift, robustness, safety, latency, reliability, cost, user adoption, and business KPIs.
• Awareness of Responsible AI, security, privacy, observability, testing, reproducibility, auditability, and human oversight required for enterprise production.
• Proven project leadership capability: solution architecture, estimation, delivery governance, resource planning, project economics, risk management, and partner coordination.
• Exceptional stakeholder engagement and communication skills - ability to translate complex AI choices into business strategy, measurable value, and executive decisions.

What We Are NOT Looking For

• Pure Data Scientists or researchers without production engineering, deployment, monitoring, or lifecycle ownership.
• MLOps, DevOps, or infrastructure‑only profiles without substantive model development, experimentation, and evaluation capability.
• Prompt‑engineering‑only or LLM‑only profiles without strong foundations in statistics, machine learning, and rigorous evaluation.
• Pure Delivery or Program Managers with no technical AI architecture, solutioning, or build experience.
• Profiles focused purely on presales or platform consulting without demonstrable end‑to‑end enterprise AI delivery depth.

Accenture is an equal opportunities employer and welcomes applications from all sections of society and does not discriminate on grounds of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, or any other basis as protected by applicable law.