New opportunity

AI / ML Engineer

Accenture · Pune, India

About this role

Accenture seeks an AI / ML Engineer for its Pune office. The role develops AI applications and systems using AI tools and cloud AI services, with cloud or on-premises pipelines built to production-ready quality. Projects may involve generative AI models, deep learning, neural networks, chatbots and image processing.

Responsibilities

Lead the development and implementation of AI and machine learning solutions that meet project requirements, including integration and deployment in production environments.
Serve as a subject-matter expert, manage and collaborate with the team, make team decisions, and solve problems across multiple teams.
Mentor junior team members and coordinate cross-functional work to align AI initiatives with project goals.

Requirements

At least 5 years of experience in Data Science; the posting lists an experience range of 5–10 years.
Proficiency in Data Science and strong knowledge of machine learning algorithms and their practical applications.
Experience with cloud platforms and deploying AI models to production; familiarity with generative AI models and deep learning frameworks.
Ability to work with large datasets, preprocess data and engineer features; competence in data science programming languages such as Python or R.
Fifteen years of full-time education are required. The position is full-time and based at the Pune office.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data ScienceMachine LearningCloud AI ServicesAI Model DeploymentGenerative AIDeep LearningNeural NetworksChatbotsImage ProcessingData PreprocessingFeature EngineeringPythonRTeam LeadershipMentoringCross-functional Collaboration

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Production-Ready AI Deployment
The role emphasizes building robust, scalable pipelines for AI models rather than just experimental research. You must demonstrate experience moving models from development environments into production, ensuring they are reliable, maintainable, and integrated with cloud or on-premises infrastructure.
Technical Leadership and Mentorship
As an SME, you are expected to guide junior team members and drive decision-making across multiple teams. You should be prepared to discuss how you have managed technical projects, resolved cross-functional conflicts, and elevated the technical capabilities of your team.
End-to-End AI Solution Development
You must be proficient in the full lifecycle of AI projects, including data preprocessing, feature engineering, and selecting appropriate architectures like deep learning or generative AI. Focus on your ability to align these technical choices with specific business project goals.

Likely challenges to think through

Balancing Innovation with Production Constraints
Integrating cutting-edge technologies like generative AI into production-ready systems often creates a trade-off between model performance and operational stability. Prepare by discussing how you manage technical debt and ensure that experimental models meet enterprise-grade reliability standards.
Cross-Functional Coordination Complexity
The requirement to engage with multiple teams suggests a high-stakes environment where AI initiatives must align with broader organizational objectives. Prepare for this by highlighting your experience in communicating complex technical concepts to non-technical stakeholders and coordinating dependencies across different departments.

Practice your answers

Technical practice

Production-Ready AI Pipelines

What to coverDiscuss your approach to transitioning a model from a research environment to a production-ready pipeline. Focus on CI/CD for ML, monitoring for model drift, and ensuring scalability within cloud environments.

Generative AI Implementation

What to coverExplain how you integrate generative AI models into existing systems. Address challenges such as prompt engineering, managing model latency, and ensuring output quality or safety in a business context.

Deep Learning Frameworks and Architectures

What to coverCompare deep learning frameworks like PyTorch or TensorFlow for specific use cases. Discuss how you select an architecture for tasks like image processing or neural network-based predictive modeling.

Data Preprocessing and Feature Engineering

What to coverDescribe your methodology for handling large, unstructured datasets. Explain how you perform feature engineering to improve model performance and how you manage data quality issues before training.

Cloud AI Services Integration

What to coverDetail your experience leveraging cloud-native AI services versus building custom models. Discuss the trade-offs regarding cost, maintenance, and performance when deploying on platforms like AWS, Azure, or GCP.

Machine Learning Algorithm Selection

What to coverExplain your process for selecting the appropriate machine learning algorithm for a given business problem. Focus on how you balance model complexity, interpretability, and accuracy requirements.

Chatbot and Conversational AI Development

What to coverDiscuss the technical stack and design patterns you use for building conversational agents. Address how you handle context management, intent recognition, and integration with backend systems.

Programming Proficiency in Python or R

What to coverProvide examples of how you have used Python or R to automate data science workflows. Highlight your ability to write clean, modular, and maintainable code suitable for team-based production environments.

Behavioural practice

Technical Leadership and Decision Making

What to coverDescribe a situation where you had to make a critical technical decision that affected multiple teams. Explain how you evaluated the options, communicated the decision, and ensured alignment with project goals.

Mentoring and Team Development

What to coverShare an example of how you mentored a junior team member to improve their technical skills. Focus on the specific actions you took to support their growth and the resulting impact on the team's output.

Cross-functional Collaboration

What to coverProvide an example of a time you coordinated with cross-functional teams to deliver an AI initiative. Explain how you managed conflicting priorities and ensured the project remained aligned with organizational objectives.

Problem Solving Across Teams

What to coverDescribe a complex technical problem that spanned multiple teams and how you facilitated the solution. Detail your role in identifying the root cause and leading the implementation of the fix.

Sharpen your knowledge

Topics to prepare

Production-Ready AI Pipelines
The role emphasizes deploying models into production environments. Review architectural patterns for CI/CD in machine learning, focusing on model versioning, monitoring, and automated testing to ensure reliability in cloud or on-premises setups.
Generative AI and Deep Learning Architectures
The position requires familiarity with GenAI and deep learning. Study the conceptual differences between transformer-based models and traditional neural networks, focusing on how these are integrated into larger application workflows.
Technical Leadership and Mentorship
As an SME, you will lead teams and mentor juniors. Prepare to discuss your experience in making high-level technical decisions, resolving cross-team conflicts, and aligning AI initiatives with broader project goals.

Skills to revise

Data Preprocessing and Feature Engineering
Practice handling large datasets using Python or R. Focus on creating robust pipelines for cleaning, transforming, and engineering features that improve model performance, as this is a core requirement for the role.
Cloud AI Model Deployment
Demonstrate your ability to deploy models using cloud services. Practice containerization (e.g., Docker) and orchestration techniques to show how you move a model from a development environment to a production-ready state.

Your preparation checklist

Prepare a production-grade ML project walkthrough
Select a past project where you deployed a model to a cloud environment. Be ready to explain your pipeline architecture, how you handled data preprocessing at scale, and the specific steps taken to ensure the solution was production-ready.
Develop a case study on Generative AI integration
Prepare a detailed explanation of how you have implemented or evaluated Generative AI models within a larger system. Focus on the technical challenges of integrating these models into existing workflows and how you managed performance or latency.
Draft examples of technical leadership and mentoring
Prepare specific anecdotes demonstrating your experience as a subject matter expert. Focus on instances where you made critical technical decisions, resolved cross-functional conflicts, or mentored junior engineers to improve team output.
Formulate questions regarding Accenture's AI infrastructure
Prepare thoughtful questions about the specific cloud platforms and MLOps tools currently used at the Pune office, and ask how the team balances rapid prototyping with the requirement for production-ready quality.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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