New opportunity

AI / ML Engineer

Accenture · Pune, India

About this role

Accenture seeks an AI / ML Engineer for a full-time role based at its Pune office. The engineer develops production-ready applications and systems using AI tools and cloud AI services, with cloud or on-premises application pipelines. The position also involves applying generative AI models to new and existing solutions.

Responsibilities

Design, build and deploy scalable AI applications and pipelines with production-level quality and reliability. Integrate generative AI models to improve application functionality and user experience; projects may also involve deep learning, neural networks, chatbots and image processing.
Act as a subject-matter expert, manage and mentor the team, make team decisions, and solve problems for the immediate team and across multiple teams.
Collaborate with cross-functional teams and stakeholders to align AI solutions with project goals, business objectives and technical requirements. Share knowledge to support team development.

Requirements

The posting lists 5–10 years of experience and requires a minimum of 5 years, including a minimum of 5 years in Python Frameworks. It requires 15 years of full-time education.
Proficiency in Python Frameworks, machine learning, large language models and generative AI. Strong experience developing and deploying AI models on cloud platforms and on-premises infrastructure; understanding of neural networks and deep learning architectures; and the ability to build reliable, scalable AI pipelines.
Experience with chatbot development, image processing and natural language processing, plus familiarity with software development best practices, version control and collaborative coding.
Technical work includes Python/FastAPI backend development, GitLab pipelines and CI/CD automation, and Azure Cloud development. The posting also calls for basic knowledge of generative AI processes such as embeddings and indexes, basic Ansible knowledge for Azure work, C#/.NET knowledge for migration of an older application, very basic React and TypeScript for a small internal frontend, and knowledge of agent-assisted development. No separate good-to-have skills are listed.

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

Skills for this role

Python FrameworksPythonFastAPIMachine LearningLarge Language ModelsGenerative AIAzureGitLabCI/CDAnsibleC#.NETReactTypeScriptEmbeddingsIndexesDeep LearningNeural NetworksChatbot DevelopmentImage ProcessingNatural Language ProcessingAI PipelinesVersion ControlAgent-assisted Development

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 delivery
The role designs, builds and deploys scalable AI applications and cloud or on-premises pipelines with production-level quality and reliability.
Generative AI integration
The engineer leads integration of generative AI models into new and existing applications to improve functionality and user experience; the posting also names embeddings, indexes and LLMs.
Backend and deployment ownership
The stated technical work includes Python/FastAPI backend development, GitLab CI/CD automation and Azure Cloud development.
Team leadership and stakeholder alignment
The engineer is expected to act as an SME, manage and mentor the team, make team decisions, contribute across teams and align solutions with business and technical requirements.
Work across existing applications
The posting calls for C#/.NET knowledge for an older application migration, plus basic React and TypeScript for a small team-internal frontend.

Likely challenges to think through

Balancing AI capability with reliability
Preparation hypothesis, not a reported employer problem: adding generative AI features while maintaining scalable, production-ready pipelines may require trade-offs between functionality and operational reliability. Prepare an example of how you would test a model integration and handle failures in a cloud or on-premises pipeline.
Keeping migration work appropriately scoped
Preparation hypothesis: an older C#/.NET application and a small internal frontend may compete for attention with the core Python and Azure work. Prepare to explain how you would identify integration boundaries and prioritize changes without assuming a full rewrite is needed.
Making decisions across teams
Preparation hypothesis: SME responsibility and cross-team decisions can create tension between technical preferences and stakeholder objectives. Prepare an example of how you would explain options, document trade-offs and help a team reach a decision.

Practice your answers

Technical practice

How would you design a production-ready AI pipeline for cloud or on-premises deployment?

What to coverThe role calls for scalable, reliable pipelines on cloud and on-premises infrastructure. Suggested preparation: explain the pipeline stages, deployment choices, and how you would test and monitor reliability.

How would you structure a Python/FastAPI backend that serves an AI model?

What to coverFastAPI backend development is listed in the posting. Suggested preparation: discuss request validation, separation of API and model logic, error handling, and how you would keep the service maintainable.

What would you put in a GitLab CI/CD pipeline for an AI application?

What to coverThe role includes GitLab pipelines, CI/CD automation, and collaborative coding. Suggested preparation: walk through code checks, tests, deployment controls, and how version control supports a safe release or rollback.

How would you use embeddings and indexes with an LLM?

What to coverThe posting asks for a basic understanding of embeddings, indexes, and LLMs. Suggested preparation: explain how content becomes searchable, how relevant context reaches the model, and how you would check whether retrieval helps the answer.

How would you evaluate a generative AI feature before adding it to an existing application?

What to coverThe role leads GenAI integration to improve functionality and user experience. Suggested preparation: define the intended improvement, compare it with the current application, and discuss quality, latency, cost, and failure cases as evaluation considerations.

How would you choose and evaluate an ML or deep-learning approach for a project?

What to coverThe posting calls for ML proficiency and an in-depth understanding of neural networks and deep-learning architectures. Suggested preparation: explain how data, task, available compute, and evaluation results would inform a simpler model versus a neural approach.

How would you develop and assess a conversational AI feature?

What to coverChatbot development and natural language processing are named in the role. Suggested preparation: describe how you would handle user input and conversational context, then assess response quality and common failure cases.

How would you approach an image-processing task within an AI application?

What to coverImage processing is listed as a possible project area. Suggested preparation: explain input preparation, a suitable processing or model approach, and how you would evaluate results; avoid assuming a particular image task.

How would you deploy an AI service on Azure while accounting for the stated Ansible requirement?

What to coverAzure development is emphasized, and basic Ansible knowledge is requested for Azure work. Suggested preparation: describe the deployment steps you would automate, what you would verify after deployment, and where Ansible could help make setup repeatable.

How would you plan the technical interface for migrating an older C#/.NET application?

What to coverThe posting mentions C#/.NET knowledge for an old-app migration and basic React/TypeScript for a small internal frontend. Suggested preparation: discuss how you would inspect existing interfaces, introduce a Python/FastAPI service if appropriate, and verify that dependent users or components still work.

Behavioural practice

Tell me about a consequential technical decision you made for a team.

What to coverThe role expects an SME who is responsible for team decisions. Suggested practice: use situation, action, and result to show the options you considered, how you involved the team, and the outcome.

Describe a time you resolved a problem spanning multiple teams.

What to coverThe posting calls for solving problems across the immediate team and other teams. Suggested practice: explain the situation, your coordinating actions, and the result, including how you handled differing priorities.

Give an example of aligning a technical solution with stakeholder objectives.

What to coverStakeholder coordination on business objectives and technical requirements is part of the role. Suggested practice: describe the situation, how you clarified or negotiated requirements, and what resulted.

Tell me about a time you mentored someone or shared technical knowledge.

What to coverThe role includes mentorship and knowledge sharing. Suggested practice: identify the learning need, the action you took, and an observable result without overstating your contribution.

Describe a time you led a team through a difficult delivery trade-off.

What to coverThe posting combines team management with production-ready, scalable AI delivery. Suggested practice: use situation, action, and result to explain the trade-off, how you made the decision, and its effect on delivery or reliability.

Sharpen your knowledge

Topics to prepare

LLMs and generative AI integration
Posting: Lead integration of generative AI models into new and existing applications; understand LLMs, embeddings and indexes. Suggested revision exercise: diagram how an application would use embeddings and an index to find relevant content before an LLM response, then explain where the integration could affect functionality and user experience.
Machine learning and deep learning
Posting: Machine learning proficiency and an in-depth understanding of neural networks and deep learning architectures are required. Suggested revision exercise: compare two neural-network architectures, explaining their inputs, outputs and practical suitability for a stated application.
NLP, chatbots and image processing
Posting: Experience with chatbot development, natural language processing and image processing is listed. Suggested revision exercise: outline one conversational-agent use case and one image-analysis use case, identifying the input, expected output and how you would check whether each works.
Production AI pipeline design
Posting: Design scalable, reliable, production-quality AI pipelines for cloud or on-premises applications. Suggested revision exercise: draw a deployment pipeline for an AI-backed application and explain how its design addresses reliability, scalability and the choice of cloud versus on-premises infrastructure.
Technical leadership and alignment
Posting: The engineer is expected to act as an SME, make team decisions, work across teams, mentor colleagues and align solutions with stakeholders’ business and technical goals. Suggested revision exercise: rehearse a design decision, including its trade-offs, how you would explain it to stakeholders and how you would share the reasoning with the team.

Skills to revise

Python and FastAPI backend development
Posting: Python Frameworks are a must-have, with FastAPI backend development specified. Suggested revision exercise: implement a small FastAPI endpoint that accepts an input and returns a model-backed response; review its error handling and code structure. The posting also mentions agent-assisted development, so be ready to explain how you would review any assisted code.
Azure and on-premises deployment
Posting: Azure Cloud development is important, and experience deploying AI models on both cloud platforms and on-premises infrastructure is required. Suggested revision exercise: walk through how you would deploy the same small AI application in each setting and identify what would change.
GitLab pipelines and CI/CD
Posting: GitLab pipelines, CI/CD automation, version control and collaborative coding are specified. Suggested revision exercise: draft a GitLab pipeline for a FastAPI application that runs checks before deployment, then explain how changes would be reviewed.
Basic Ansible for Azure work
Posting: Basic Ansible knowledge is requested for work with Azure Cloud. Suggested revision exercise: read or draft a small Ansible playbook for a repeatable setup task, and explain what it changes and how you would verify the result.
C#/.NET migration reading
Posting: C#/.NET knowledge is requested for migration of an older application. Suggested revision exercise: trace a small C#/.NET application's request and response flow, then identify the behavior that a migrated component would need to preserve.
Basic React and TypeScript
Posting: Very basic React and TypeScript understanding is requested for a small team-internal frontend. Suggested revision exercise: build a simple typed form that calls a FastAPI endpoint and displays its response.

Your preparation checklist

Prepare a requirements evidence sheet
The posting specifies 5–10 years of experience, at least five years in Python Frameworks, and 15 years of full-time education. Prepare a factual timeline and examples of work with Python frameworks, ML, LLMs, and generative AI; do not infer eligibility from this checklist.
Build a production AI project walkthrough
The role calls for scalable, reliable AI applications and pipelines on cloud or on-premises infrastructure. Prepare one architecture diagram and a concise account of your design decisions, deployment, reliability measures, and results.
Practice a generative AI design exercise
The posting mentions integrating GenAI into new or existing applications and basic knowledge of embeddings and indexes. Sketch an application flow, explain where an LLM fits, and identify how you would evaluate functionality and user experience.
Explain your delivery workflow
FastAPI backend development, Azure Cloud, GitLab pipelines, CI/CD automation, and basic Ansible knowledge are listed. Prepare a concrete deployment walkthrough showing code changes, automated checks, release steps, and how you would diagnose a failure.
Select examples across the stated AI areas
The posting names neural networks, deep learning, chatbots, image processing, and NLP. Prepare brief examples from relevant work, distinguishing hands-on contributions from approaches you would take where you lack a project example.
Review the adjacent technology boundaries
The role mentions C#/.NET for an older-app migration, very basic React/TypeScript for an internal frontend, and agent-assisted development. Prepare a short explanation of relevant experience and questions about how these components connect to the AI backend.
Prepare a leadership and stakeholder example
The engineer is expected to act as an SME, make team decisions, mentor others, and coordinate across teams. Prepare a decision walkthrough covering the trade-off, stakeholder input, your contribution, and the outcome.
Write focused questions for the employer
Ask which AI applications are the immediate priority, how cloud and on-premises deployments are divided, what production reliability means for the team, and how the older-app migration fits the role.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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