New opportunity

AI / ML Engineer

Accenture · Pune, India

About this role

Accenture seeks an AI / ML Engineer in Pune to develop production-ready applications and systems using AI tools, cloud AI services, and cloud or on-premises application pipelines. The role emphasizes large language models and may involve generative AI, deep learning, neural networks, chatbots, and image processing.

Responsibilities

Design, implement, and improve AI applications and models while meeting production quality standards.
Integrate AI solutions into existing systems and workflows with cross-functional teams.
Serve as a subject-matter expert, manage team performance, make team decisions, and contribute to decisions involving multiple teams.
Solve problems for the immediate team and across teams, and mentor junior colleagues.

Requirements

5–10 years of experience is listed for the position, with a minimum of 5 years required. The posting also specifies a minimum of 5 years of experience in large language models.
Proficiency in large language models; understanding of deep learning frameworks such as TensorFlow or PyTorch; familiarity with natural language processing techniques and tools; and experience developing and deploying chatbots and conversational agents.
15 years of full-time education.

Preferred

Experience with cloud-based AI services.

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

Skills for this role

Large Language Models (LLMs)Generative AICloud AI servicesDeep LearningNeural NetworksTensorFlowPyTorchNatural Language ProcessingChatbotsConversational AgentsImage ProcessingTeam LeadershipMentoring

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

Deliver production-ready LLM applications
The posting names LLMs as the required skill and calls for AI applications built to production quality. It also cites experience developing and deploying chatbots and conversational agents.
Evaluate and improve AI solutions
The role explicitly includes continuously evaluating and improving existing AI models and applications while maintaining quality standards.
Integrate solutions into existing systems
The engineer develops cloud or on-premises application pipelines and works with cross-functional teams to integrate AI solutions into established workflows. Cloud-based AI services are listed as good to have.
Lead technical and team decisions
The posting expects a subject-matter expert who manages team performance, makes team decisions, contributes to decisions across teams, solves shared problems, and mentors junior colleagues.

Likely challenges to think through

Balancing model improvements with production quality
Preparation hypothesis: An improvement to an LLM application may not be worthwhile if it compromises production quality. Prepare an example of how you would compare a proposed change with the current application and decide whether to deploy it.
Fitting AI pipelines into existing workflows
Preparation hypothesis: A cloud or on-premises pipeline may need to accommodate constraints in an existing system rather than follow an ideal standalone design. Prepare to explain how you would identify integration constraints and make a defensible design trade-off.
Making decisions across teams
Preparation hypothesis: The role’s team and cross-team responsibilities may require resolving competing priorities while keeping delivery moving. Prepare an example of how you would explain a technical decision, address objections, and support junior colleagues through implementation.

Practice your answers

Technical practice

How would you design a production-ready application that uses an LLM?

What to coverThe role calls for production-ready AI applications and systems. Suggested preparation: explain the application components, how the model connects to them, and how you would address reliability, quality and operational trade-offs.

How would you choose between a cloud AI service and a cloud or on-premises application pipeline?

What to coverThe posting mentions cloud AI services and cloud or on-premises pipelines; cloud-service experience is preferred. Suggested preparation: compare integration effort, operational control and deployment constraints without assuming a particular provider.

How would you evaluate and improve an existing LLM application?

What to coverThe role includes continuously evaluating and improving AI models and applications. Suggested preparation: describe how you would define task-relevant quality measures, inspect failure cases and check whether a change improves results.

How would you investigate a production chatbot that has begun giving poor answers?

What to coverThe posting calls for chatbot development and deployment as well as production quality. Suggested preparation: distinguish possible causes in inputs, the model and application integration, then explain how you would test a fix before release.

How would you select an LLM for a generative-AI application?

What to coverLLM proficiency is required, and the role may apply generative-AI models. Suggested preparation: discuss how the use case, response quality, latency and deployment constraints would shape a model choice and its evaluation.

How would you use TensorFlow or PyTorch when developing an AI model?

What to coverThe posting asks for a strong understanding of deep-learning frameworks such as TensorFlow or PyTorch. Suggested preparation: walk through model development and testing in a framework you know, including how you would diagnose training or inference problems.

Which NLP techniques would you use to prepare text for an LLM application?

What to coverThe posting asks for familiarity with NLP techniques and tools. Suggested preparation: explain how text preparation and handling of inputs would depend on the task, and how you would check that those choices preserve useful information.

How would you develop and deploy a conversational agent?

What to coverExperience developing and deploying chatbots and conversational agents is stated in the posting. Suggested preparation: cover conversation flow, handling unclear requests, integration with the application and tests of the deployed experience.

How would you integrate an AI solution into an existing workflow?

What to coverThe role involves integrating AI solutions into existing systems and workflows with cross-functional teams. Suggested preparation: describe how you would establish interfaces, handle failures at system boundaries and verify that the workflow still works end to end.

Behavioural practice

Tell me about a time you helped a technical team perform better.

What to coverThe role expects collaboration and management of team performance. Suggested preparation: use a real situation, your actions and the result; distinguish your contribution from the team's and explain how you assessed improvement.

Describe a significant technical decision you made for a team.

What to coverThe posting says the role is responsible for team decisions. Suggested preparation: give a real example covering the options, trade-offs, how you involved others and the outcome.

Tell me about a time you integrated a solution with another team's workflow.

What to coverCross-functional integration is part of the advertised work. Suggested preparation: explain the situation, how you aligned requirements and responsibilities, what you did when constraints conflicted, and the result.

Describe a problem you helped resolve across multiple teams.

What to coverThe role calls for solving problems beyond the immediate team and contributing to decisions involving multiple teams. Suggested preparation: give a real example of the problem, your actions to coordinate a resolution and the observable result.

Tell me about a time you mentored a junior colleague.

What to coverMentoring junior team members is an explicit responsibility. Suggested preparation: describe the colleague's learning need, the support you provided and the result, without claiming credit for their work.

Sharpen your knowledge

Topics to prepare

Large language models
Must-have proficiency, with a stated minimum of five years of LLM experience. Revise by explaining how you would select, assess and improve an LLM for an application, including how you would identify inadequate outputs.
Production-ready AI architecture
The role develops production-ready systems using AI tools and cloud AI services, with cloud or on-premises application pipelines. Sketch an architecture and explain its deployment, integration and quality considerations.
Deep learning and NLP
The posting calls for a strong understanding of frameworks such as TensorFlow or PyTorch and familiarity with NLP techniques and tools. Review how model training and inference work in one named framework, then explain which NLP approaches suit a conversational task.
Conversational-agent design
The posting specifies experience developing and deploying chatbots and conversational agents. Map a conversation flow and explain how you would handle ambiguous requests and assess response quality.
Technical leadership and decisions
The role expects subject-matter expertise, team decisions, cross-team problem-solving and mentoring. Rehearse explaining a technical trade-off, how you would reach a decision with other teams, and how you would guide a junior colleague.

Skills to revise

Evaluate and improve an LLM application
As a preparation exercise, use a small set of representative prompts to inspect outputs, record recurring failures and propose a targeted improvement. This practices the posting’s requirement to continuously evaluate and improve AI models and applications; the exercise is not a stated employer test.
Build a conversational-agent example
As a preparation exercise, implement a small chatbot flow and demonstrate how it handles expected and unclear inputs. This relates to the stated experience in developing and deploying chatbots and conversational agents, without implying a particular platform is required.
Plan an application integration
As a preparation exercise, draw the interfaces between an AI component, an existing workflow and a cloud or on-premises pipeline; walk through how you would check production quality. The posting explicitly calls for integration across systems and production-ready delivery.
Explain a team-level technical decision
As a preparation exercise, prepare a concise example of weighing two AI solution approaches, communicating the decision across teams and mentoring someone through the implementation. This reflects the advertised SME, decision-making and mentoring responsibilities.

Your preparation checklist

Map the stated requirements to evidence
Prepare a dated experience summary addressing the posting’s minimum five years in large language models, plus documentation of 15 years of full-time education. Note the role is listed in Pune at the team lead/consultant level; do not assume the summary alone establishes eligibility.
Prepare a production AI project walkthrough
Choose one relevant project and explain the problem, your decisions, the LLM or other AI components, integration into an existing workflow, deployment pipeline, and measurable results. The posting emphasizes production-ready quality and continuous improvement.
Practice an LLM solution design
As an interview exercise, sketch an application using an LLM and a cloud or on-premises pipeline. Explain model choices, integration points, quality checks, and how you would improve the solution after release; these are preparation prompts, not specified interview tasks.
Organize focused technical examples
Prepare concise explanations of your work or understanding in NLP, TensorFlow or PyTorch, and chatbot or conversational-agent development and deployment. Distinguish direct experience from conceptual knowledge; cloud-based AI services are listed as good to have.
Rehearse evaluation and troubleshooting decisions
Use a project example to show how you identified a model or application problem, assessed its impact, made a change, and checked the outcome. This aligns with the stated responsibility to evaluate and improve existing AI models and applications.
Document team-level leadership examples
Prepare examples of an SME decision, cross-functional collaboration, a problem resolved across teams, and mentoring a junior colleague. State your specific contribution and the result rather than claiming sole ownership of a team outcome.
Write questions about the role’s scope
Ask which AI applications are priorities, how cloud versus on-premises pipelines are chosen, what production-quality measures matter, and how team decisions and mentoring responsibilities are divided. These questions seek details the posting does not provide.

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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