New opportunity

AI / ML Engineer

Accenture · Hyderabad, India

About this role

Accenture seeks an AI / ML Engineer based at its Hyderabad office to develop production-ready applications and systems using AI tools, cloud AI services, and generative AI models. Solutions may also involve deep learning, neural networks, chatbots, and image processing.

Responsibilities

Design and implement scalable AI solutions with production-quality cloud or on-premises application pipelines, integrating them into existing systems and workflows.
Serve as a subject-matter expert, manage and collaborate with the team, and take responsibility for team decisions.
Work across multiple teams on key decisions and solve problems that affect multiple teams.
Share knowledge, conduct training, and monitor project progress against strategic goals.

Requirements

Proficiency in large language models is mandatory. The posting states a minimum of 12 years of experience is required and separately specifies a minimum of 12 years in large language models; its job header lists 10–12 years of experience.
Experience with cloud-based AI services and deployment; strong understanding of deep learning frameworks such as TensorFlow or PyTorch; familiarity with natural language processing; and ability to design and implement scalable AI solutions.
15 years of full-time education.

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 servicesCloud deploymentDeep learningTensorFlowPyTorchNatural language processingNeural networksChatbotsImage processingTeam management

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 pipelines
The role develops AI applications with production-quality cloud or on-premises pipelines. Prioritize explaining how you design, deploy, and assess the quality of an end-to-end solution.
LLM proficiency in scalable solutions
Large language models are the stated must-have skill, alongside the ability to design scalable AI solutions. Prepare to explain technical choices for applying an LLM within an application or system.
Integration across existing workflows
The posting emphasizes integrating AI solutions into existing systems and workflows through cross-functional collaboration. Focus on how technical and integration decisions are coordinated with other teams.
Team-level technical leadership
The engineer is expected to act as an SME, manage and collaborate with the team, own team decisions, and solve problems spanning multiple teams. Prepare examples that distinguish your decisions from the team's collective work.
Capability building and delivery oversight
Responsibilities include knowledge sharing, training, and monitoring project progress against strategic goals. Be ready to describe how technical guidance and progress tracking inform delivery decisions.

Likely challenges to think through

Balancing model capability with production quality
Preparation hypothesis: applying GenAI models while delivering scalable, production-ready pipelines may require trade-offs between solution capability and operational complexity. Prepare to explain how you would evaluate those trade-offs; the posting does not prescribe a particular method.
Resolving cross-team integration decisions
Preparation hypothesis: changes to existing workflows may create competing needs across teams. Prepare an example of how you would surface dependencies, make a decision, and communicate its impact without assuming a specific Accenture process.
Choosing the right AI approach
Preparation hypothesis: the role centers on LLMs but may also involve deep learning, chatbots, or image processing. Prepare to explain when an LLM is appropriate and when another approach might better fit the application; the posting does not say every project uses all of these technologies.

Practice your answers

Technical practice

How would you choose an LLM for an application?

What to coverLLM proficiency is a required skill for this role. Preparation suggestion: explain how the intended task, output quality, latency, cost and deployment constraints would shape your choice, and what you would test before committing.

How would you evaluate a generative AI solution before release?

What to coverThe role involves generative AI models and production-ready quality. Preparation suggestion: describe task-specific test cases, review of inaccurate or unsafe outputs, and how evaluation results would inform a release decision.

How would you design a production-ready AI application pipeline?

What to coverThe posting calls for production-quality cloud or on-premises application pipelines. Preparation suggestion: walk through data and model inputs, application components, testing, deployment and monitoring, explaining where failures could occur.

When would you use a cloud AI service rather than an on-premises approach?

What to coverThe role mentions cloud AI services and cloud or on-premises pipelines. Preparation suggestion: compare operational responsibility, integration constraints, scalability and cost without assuming a particular provider or architecture.

How would you make an AI solution scalable?

What to coverDesigning and implementing scalable AI solutions is a stated skill. Preparation suggestion: explain how you would identify bottlenecks, test capacity and balance response time, reliability and resource use.

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

What to coverThe posting emphasizes integration into existing systems and workflows across teams. Preparation suggestion: discuss interface requirements, data flow, failure handling and how you would validate that the integration works for its users.

How would you approach implementation with TensorFlow or PyTorch?

What to coverThe posting asks for a strong understanding of deep learning frameworks such as TensorFlow or PyTorch. Preparation suggestion: use a relevant model-development example to explain framework capabilities, training and inference considerations, and how you would debug an implementation issue.

How would you apply NLP techniques to an LLM-based application?

What to coverThe posting requires familiarity with natural language processing techniques and tools. Preparation suggestion: explain how text preparation, task formulation and evaluation choices affect application quality, using a clearly identified example rather than assuming a specific project.

How would you diagnose a decline in an AI application's output quality?

What to coverThe role is responsible for production-ready solutions that meet quality standards. Preparation suggestion: describe how you would isolate changes in inputs, model behavior and application components, then verify a fix before deployment.

How would you design a chatbot that uses a generative AI model?

What to coverThe posting says solutions could include chatbots; it does not make them a required project. Preparation suggestion: discuss conversation context, response-quality checks and integration with an existing workflow as one possible application.

Behavioural practice

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

What to coverThe role expects responsibility for team decisions. Preparation suggestion: give the situation, your decision-making actions and the result, including how you handled differing technical views.

Describe a time you coordinated a decision across multiple teams.

What to coverThe posting calls for engagement with multiple teams on key decisions. Preparation suggestion: explain the situation, how you clarified dependencies and reached a decision, and what changed as a result.

Tell me about a problem you solved that affected more than one team.

What to coverThe role expects solutions to problems that apply across teams. Preparation suggestion: distinguish the shared problem from local symptoms, describe your actions and state the observed result.

Describe a knowledge-sharing or training session you led.

What to coverFacilitating knowledge sharing and training is a stated responsibility. Preparation suggestion: explain the team's need, how you adapted the session to it and what evidence showed whether it helped.

Tell me about a project whose progress needed to be brought back in line with its goals.

What to coverThe role includes monitoring project progress and alignment with strategic goals. Preparation suggestion: describe the situation, the signals you used to identify a gap, the actions you took and the resulting change.

Sharpen your knowledge

Topics to prepare

Large language models and generative AI
Posting: LLM proficiency is a must-have, and solutions may use generative AI models. Revision exercise (suggestion): explain how you would choose and evaluate an LLM for an application, including how you would check output quality.
Scalable, production-ready AI architecture
Posting: the role designs scalable AI solutions and production-quality cloud or on-premises pipelines that integrate with existing workflows. Revision exercise (suggestion): sketch a system diagram and explain its integration points, capacity considerations and quality checks.
Deep learning and NLP foundations
Posting: a strong understanding of TensorFlow or PyTorch and familiarity with NLP are listed; solutions may also involve neural networks. Revision exercise (suggestion): compare how you would prepare text, train a model and assess its results for a small NLP task.
Technical decisions across teams
Posting: the engineer is expected to act as an SME, take responsibility for team decisions, solve cross-team problems and share knowledge. Revision exercise (suggestion): prepare a short explanation of an AI design decision, its trade-offs and how you would communicate it to affected teams.

Skills to revise

Build an LLM application slice
Posting: the role develops applications using AI tools and generative AI models. Practice exercise (suggestion): implement a small application flow that accepts input, calls an LLM and checks the resulting output against defined quality criteria.
Assemble an AI deployment pipeline
Posting: cloud AI services, deployment and production-ready cloud or on-premises pipelines are explicit. Practice exercise (suggestion): connect a model-backed application to an available cloud AI service, or an on-premises runtime, and test how it handles errors and repeated requests.
Implement an NLP model in a named framework
Posting: TensorFlow or PyTorch understanding and NLP familiarity are requested. Practice exercise (suggestion): use either framework to build and evaluate a small text-classification model, then explain the training and evaluation choices.
Rehearse a cross-team technical review
Posting: the role includes team decisions, knowledge sharing and monitoring progress against strategic goals. Practice exercise (suggestion): present a brief AI solution plan, invite objections from another team's perspective and summarize the decision and progress measures.

Your preparation checklist

Prepare an LLM project walkthrough
The posting makes LLM proficiency mandatory. Prepare one relevant example covering the problem, model choices, implementation, evaluation, deployment, and your decisions; use only work you can substantiate.
Sketch a production AI architecture
The role calls for scalable, production-ready cloud or on-premises pipelines integrated with existing workflows. As a practice exercise, diagram components, deployment, quality checks, and integration points, and explain your trade-offs.
Gather evidence for adjacent technical skills
Prepare concise examples of cloud AI service deployment, TensorFlow or PyTorch, and NLP techniques where you have direct experience. The posting lists these skills but does not prescribe particular tools or projects.
Rehearse a cross-team decision story
The role expects an SME who manages and collaborates with a team and contributes to decisions affecting multiple teams. Prepare a specific example showing the decision, stakeholders, trade-offs, and outcome.
Outline a knowledge-sharing example
The posting mentions training, knowledge sharing, and tracking progress against strategic goals. Prepare an example or a clearly labeled proposed approach showing how you would explain technical choices and monitor progress.
Write targeted questions for Accenture
Ask what production systems and cloud or on-premises environments this Hyderabad role supports, how team decision-making works, and how the listed 10–12 years in the header relates to the separate minimum 12 years of experience and 12 years in LLMs.

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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