New opportunity

AI / ML Engineer

Accenture · Jaipur, India

About this role

Accenture is hiring an AI / ML Engineer for its Jaipur office. The role develops production-ready applications and systems using AI tools and cloud AI services, with cloud or on-premises application pipelines. Solutions may incorporate generative AI models, deep learning, neural networks, chatbots and image processing.

Responsibilities

Design, implement and integrate scalable AI applications that meet production-quality standards.
Serve as a subject-matter expert, collaborate with and manage the team, and take responsibility for team decisions.
Work with multiple teams on key decisions and solve problems within and across teams.
Mentor junior colleagues and improve existing processes to increase efficiency.

Requirements

Proficiency in large language models (LLMs), with a minimum of 7.5 years of experience required; the posting also specifies at least 7.5 years of LLM experience.
Strong understanding of deep learning frameworks such as TensorFlow or PyTorch; experience deploying AI solutions on AWS, Azure or Google Cloud; and familiarity with natural language processing techniques and tools.
Ability to design and implement scalable AI applications and completion of 15 years of full-time education.

This is a full-time position based at the Jaipur office.

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 AIDeep LearningNeural NetworksTensorFlowPyTorchAWSMicrosoft AzureGoogle CloudNatural Language ProcessingChatbotsImage ProcessingCloud AI ServicesAI Application DevelopmentTeam 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

Production-ready AI applications
Posting: Design, implement and integrate scalable AI applications using cloud AI services or cloud/on-premises pipelines. Prepare: Be ready to explain how you would move a solution from design into an existing system while maintaining production quality.
LLM and generative AI expertise
Posting: LLM proficiency is the required skill, and solutions may incorporate generative AI models. Prepare: Focus on the reasoning behind model choices and how their outputs would be evaluated in an application.
Practical deployment across the AI stack
Posting: The role calls for cloud deployment experience, understanding of TensorFlow or PyTorch, and familiarity with NLP tools and techniques. Prepare: Connect these technologies to application design rather than presenting them as a standalone skill list.
Technical leadership and team improvement
Posting: The engineer is expected to act as an SME, take responsibility for team decisions, work across teams, mentor junior colleagues and improve processes. Prepare: Use concrete examples of how technical decisions were made and communicated.

Likely challenges to think through

Balancing model capability with production quality
Preparation hypothesis, not a reported Accenture problem: A generative AI approach may look promising before it is reliable enough for an integrated application. Prepare to explain trade-offs among output quality, evaluation, scalability and operational constraints.
Integrating across deployment environments
Preparation hypothesis, not a confirmed project constraint: Cloud AI services and cloud or on-premises pipelines can create different integration choices. Prepare to explain how you would choose an approach while accounting for existing systems and production requirements.
Making decisions across teams
Preparation hypothesis, not a reported team issue: As an SME responsible for team decisions, the engineer may need to reconcile different technical priorities without slowing delivery. Prepare an example of clarifying trade-offs, reaching a decision and helping others carry it out.

Practice your answers

Technical practice

How would you choose an LLM for a production application?

What to coverSuggested preparation: Explain how you would compare task fit, output quality, latency, cost and deployment constraints. Describe how you would validate the choice against the application's requirements rather than relying on a model's general reputation.

How would you evaluate a generative AI solution before release?

What to coverSuggested preparation: Describe representative test cases, quality measures and review of problematic outputs. Explain the trade-off between improving output quality and meeting production performance requirements.

How would you integrate an AI application into an existing system?

What to coverSuggested preparation: Walk through the application pipeline, interfaces with existing components and steps needed to reach production-ready quality. Explain how you would test the integration and handle failures at system boundaries.

How would you plan a cloud or on-premises AI application pipeline?

What to coverSuggested preparation: Compare the implications of the two deployment settings for integration, operations and scalability. Keep the answer grounded in the posting's cloud-or-on-premises scope rather than assuming a particular infrastructure.

How would you make an AI application scalable?

What to coverSuggested preparation: Explain how you would identify bottlenecks and test behaviour as demand increases. Discuss trade-offs among responsiveness, capacity and resource use for the application you are designing.

How would you deploy an AI solution on a cloud platform?

What to coverSuggested preparation: Use a platform you know among AWS, Azure or Google Cloud to explain a deployment approach and how you would verify it works. Focus on decisions relevant to operating an AI application, without assuming the role uses a specific cloud service.

When would you use TensorFlow or PyTorch in a solution?

What to coverSuggested preparation: Explain how a deep learning framework supports model development and integration into an application. Compare practical considerations for a relevant task without claiming that either framework is mandated.

How would you apply NLP techniques to an LLM application?

What to coverSuggested preparation: Describe how text preparation and task-specific evaluation could affect the application's results. Explain how you would diagnose whether a weak result comes from the input, the model or the surrounding application.

How would you design a chatbot using generative AI?

What to coverSuggested preparation: Treat chatbots as a possible project type in the posting. Explain how you would manage conversation context, assess response quality and handle responses that do not meet the application's requirements.

How would you troubleshoot an image-processing component?

What to coverSuggested preparation: Treat image processing as another possible project type, not a guaranteed assignment. Describe how you would isolate input, model and integration issues and decide whether a fix improves end-to-end quality.

Behavioural practice

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

What to coverSuggested preparation: Give the situation, the alternatives you considered and the action you took as the decision owner. State the result and how you assessed whether the decision worked.

Describe a decision you made with multiple teams.

What to coverSuggested preparation: Set out the situation and the differing priorities you needed to reconcile. Explain your actions, how the decision was reached and its result.

Tell me about solving a problem that affected more than your immediate team.

What to coverSuggested preparation: Describe the situation, how you identified the wider impact and what you did with the affected teams. Give the result and distinguish your contribution from the team's collective work.

Describe a time you mentored a junior colleague.

What to coverSuggested preparation: Explain the situation, the specific guidance or support you provided and why you chose that approach. Describe the observed result without overstating your role in their progress.

Tell me about a process you improved to increase efficiency.

What to coverSuggested preparation: Describe the original process and the problem it created. Explain the change you made, how you involved the team and what result you observed.

Sharpen your knowledge

Topics to prepare

LLM fundamentals and trade-offs
Required: LLM proficiency is the must-have skill. Revise how to select a model, assess output quality and explain limitations; practise defending those trade-offs for a production AI application.
Scalable, production-ready AI architecture
The role calls for scalable applications, integration with existing systems, and cloud or on-premises pipelines. Sketch an architecture and explain how you would handle reliability, scaling and quality checks.
Deep learning and NLP concepts
The posting asks for strong understanding of TensorFlow or PyTorch and familiarity with NLP. Explain a neural-network training and inference workflow, then identify NLP considerations for an LLM-based application.
Cloud deployment choices
Experience deploying AI solutions on AWS, Azure or Google Cloud is listed. Compare a cloud deployment with an on-premises pipeline for the same application, noting operational trade-offs without assuming a particular service.
Technical leadership and decisions
The role expects an SME who owns team decisions, works across teams, mentors juniors and improves processes. Rehearse explaining a technical decision, how you would resolve disagreement and how you would review its outcome.

Skills to revise

Build an LLM application slice
Practise implementing a small LLM-powered application flow and testing representative inputs and outputs. This directly exercises the required LLM proficiency and production-ready application work.
Implement with a deep learning framework
Using TensorFlow or PyTorch, practise building or adapting a small neural-network component and explaining its training and inference steps. Both frameworks are examples named in the posting; it does not require both.
Plan and validate deployment
Using a cloud platform you know from AWS, Azure or Google Cloud, rehearse how you would package, deploy and check an AI application. Relate each step to the stated deployment experience and cloud pipeline work.
Integrate and review an AI pipeline
Practise tracing inputs, model output and downstream integration in a cloud or on-premises application pipeline; identify a quality failure and propose a fix. This reflects the role’s integration and production-quality responsibilities.
Lead a technical review
As interview preparation, run a short mock review: present an AI design choice, invite objections, decide on a path and explain it to a junior colleague. This is a suggested exercise for the stated team-decision and mentoring responsibilities, not a specified employer task.

Your preparation checklist

Map the stated requirements to evidence
The posting requires LLM proficiency, at least 7.5 years of LLM experience, and 15 years of full-time education. Prepare a dated project timeline and education record so you can describe your background accurately; do not assume how the employer will verify it.
Build a production AI project walkthrough
The role develops and integrates production-ready AI applications. Prepare one project narrative covering the problem, your decisions, system integration, quality measures, deployment, and what you would improve.
Practice a scalable application design
As a preparation exercise, sketch an LLM application with a cloud or on-premises pipeline. Explain how you would address scalability, production quality, integration with existing systems, and deployment trade-offs.
Prepare a technical deep dive
The posting names LLMs, NLP, TensorFlow or PyTorch, and deployment on AWS, Azure or Google Cloud. Choose work you can explain firsthand and prepare concise examples of model or framework choices, testing, and deployment.
Document leadership decisions
The role expects subject-matter expertise, team decisions, cross-team problem-solving, mentoring, and process improvement. Prepare examples that distinguish your decisions from team contributions and describe their outcomes.
Draft questions about the work
Ask which AI application types are priorities, how cloud versus on-premises deployment is chosen, what production-quality standards apply, and how this role shares decisions across teams.

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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