New opportunity

AI / ML Engineer

Accenture · Navi Mumbai, India

About this role

Accenture is hiring an AI / ML Engineer to develop production-ready AI applications and systems using AI tools, cloud AI services, and cloud or on-premises pipelines. The role centers on applying generative AI models and large language models to deliver scalable solutions. The posting lists Navi Mumbai as the job location and also states that the position is based at its Mumbai office.

Responsibilities

  • Design and implement AI-driven projects aligned with organizational objectives, and integrate solutions into existing systems.
  • Develop and optimize reliable, scalable AI application pipelines for production use.
  • Serve as a subject-matter expert, manage and mentor the team, make team-level decisions, and help solve problems across multiple teams.
  • Collaborate with other teams on technical decisions and coordinate cross-functional implementation.

Requirements

  • Proficiency in generative AI and large language models. A minimum of 7.5 years of experience is required; the posting also specifies at least 7.5 years of experience in generative AI.
  • Experience developing and deploying AI applications with cloud AI services and on-premises infrastructure, plus an in-depth understanding of deep learning frameworks and neural network architectures.
  • Ability to design production-grade AI pipelines, experience with conversational AI, chatbots and natural language processing, and familiarity with image processing techniques.
  • Fifteen 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

Generative AILarge Language Models (LLMs)Cloud AI servicesAI application developmentAI deploymentDeep LearningNeural NetworksAI pipelinesConversational AIChatbotsNatural Language ProcessingImage ProcessingTeam leadershipMentoringCross-functional collaboration

Your skill match

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Production-ready generative AI

    The role calls for proficiency in generative AI and LLMs and for applying GenAI models in applications and systems built to production-ready quality.

  • Reliable, scalable AI pipelines

    Designing and optimizing production-grade pipelines is an explicit requirement, with reliability and scalability called out in the posting.

  • Deployment across cloud and on-premises environments

    The posting requires experience developing and deploying AI applications using cloud AI services and on-premises infrastructure.

  • Technical leadership and integration

    The engineer is expected to act as a subject-matter expert, make team decisions, mentor junior colleagues, work across teams, and lead projects that align with organizational objectives and integrate into existing systems.

Likely challenges to think through

  • Moving from a model demonstration to a dependable application

    A likely difficulty is balancing GenAI capability with the reliability and scale expected of a production pipeline. Prepare to explain how you would evaluate a solution and address failures before deployment; these are preparation topics, not stated employer problems.

  • Choosing an appropriate deployment approach

    Because the role spans cloud services and on-premises infrastructure, technical choices may involve different operational constraints. Prepare a comparison of how you would assess deployment options for a given application, without assuming a particular platform or architecture is in use.

  • Leading decisions across team boundaries

    Aligning an AI project with objectives while integrating it into existing systems may require trade-offs among teams. Prepare a concrete example of how you would make and communicate a technical decision, coordinate implementation, and support less-experienced colleagues.

03

Practice your answers

Technical practice

How would you design a generative AI application around an organizational objective?

What to coverSuggested preparation: Explain how you would translate the objective into an application design, select where a generative AI model adds value, and define ways to assess the result. Discuss how the design would fit existing systems rather than treating the model as a standalone solution.

How would you choose and apply an LLM for a production application?

What to coverSuggested preparation: Compare candidate models against the application's task, output quality, latency and operating constraints. Explain how you would evaluate the chosen model on representative inputs before deployment.

What would you include in a production-grade AI application pipeline?

What to coverSuggested preparation: Walk through the stages from inputs and model execution to application integration and monitoring. Explain design choices that support the reliability and scalability explicitly called for in the posting.

How would you decide between cloud AI services and on-premises infrastructure?

What to coverSuggested preparation: Compare deployment options in terms of integration, operational control, scalability and application constraints. Describe how your choice would affect the pipeline and ongoing support without assuming a particular provider.

How would you troubleshoot an AI pipeline that becomes unreliable in production?

What to coverSuggested preparation: Describe how you would isolate failures across application inputs, model execution, infrastructure and downstream integration. Explain how you would verify a fix and reduce recurrence.

How would you scale an AI application while maintaining acceptable performance?

What to coverSuggested preparation: Discuss how you would identify bottlenecks and assess trade-offs among throughput, latency, resource use and output quality. Relate your answer to the posting's requirement for scalable, production-ready systems.

How do neural network architectures and deep learning frameworks affect solution design?

What to coverSuggested preparation: Explain how architectural choices relate to the task, available data and deployment constraints. Use a relevant example to show what you would evaluate, without assuming the role requires a specific framework.

How would you design and evaluate a conversational AI application?

What to coverSuggested preparation: Cover how the application would interpret user input, manage a conversation and produce useful responses. Explain how you would test response quality and handle cases the chatbot cannot resolve.

When would image processing be useful in an AI solution?

What to coverSuggested preparation: Describe a plausible image-based use case and the processing and evaluation decisions it would involve. Treat this as a breadth topic: the posting calls for familiarity with image processing, not a named project or technique.

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

What to coverSuggested preparation: Explain how you would identify system interfaces, coordinate dependencies and test the end-to-end behavior. Discuss trade-offs between changing the AI component and changing the surrounding application.

Behavioural practice

Tell me about a time you led a technical decision for a team.

What to coverSuggested preparation: Use a real situation to explain the options, your decision-making actions and the result. Clarify how you involved the team and took responsibility for the decision.

Describe a time you resolved a problem spanning multiple teams.

What to coverSuggested preparation: Set out the shared problem, the actions you took to coordinate people and decisions, and the result. Distinguish your contribution from the work of other teams.

Tell me about a time you mentored a junior colleague.

What to coverSuggested preparation: Describe the colleague's development need, what you did to support learning and the observable result. Focus on knowledge sharing rather than a general claim about your leadership style.

Describe a time you aligned a technical implementation with a project goal.

What to coverSuggested preparation: Explain the situation and goal, how you worked with stakeholders to shape the implementation, and what resulted. Include any trade-off you had to communicate.

Tell me about a time you coordinated a cross-functional system integration.

What to coverSuggested preparation: Outline the integration challenge, your actions to manage dependencies and resolve disagreements, and the outcome. Choose an example that shows how you kept the participating teams aligned.

02

Sharpen your knowledge

Topics to prepare

  • Generative AI and LLMs

    Must-have skills in the posting. Explain how a generative model fits into an application, then compare two possible designs for the same use case and their limitations.

  • Production pipeline design

    The role calls for reliable, scalable, production-grade AI pipelines. Sketch a pipeline and explain where failures, capacity limits and integration issues could arise.

  • Cloud and on-premises deployment

    The posting specifies cloud AI services and on-premises infrastructure. Compare how deployment, dependencies and operational responsibilities would differ for one AI application.

  • Deep learning and neural networks

    An in-depth understanding of frameworks and architectures is listed. Revisit the architecture of a model you know and explain its training, inference and framework-level trade-offs.

  • Conversational AI and NLP

    Chatbots and natural language processing are explicitly listed. Map a user conversation from input through language processing to response, identifying likely failure cases.

  • Image processing

    The posting asks for familiarity, rather than naming a specific image task. Review one image-processing technique and explain when it would be useful in an AI solution.

Skills to revise

  • Build an LLM-backed application

    As a preparation exercise, implement a small application that uses a generative model and document its inputs, outputs and limitations. This practices the role's must-have GenAI and LLM skills.

  • Make a pipeline resilient

    As a preparation exercise, take a small AI application pipeline, test a failed component and increased request volume, then revise the design. This reflects the stated reliability and scalability responsibility.

  • Plan deployment across environments

    As a preparation exercise, write deployment plans for the same application using a cloud AI service and on-premises infrastructure. The posting calls for experience developing and deploying in both settings.

  • Implement a conversational flow

    As a preparation exercise, build a short chatbot interaction and test ambiguous or unexpected inputs. This connects to the listed conversational AI and NLP experience.

  • Apply an image-processing technique

    As a preparation exercise, process a small set of images and inspect cases where the technique fails. This is a focused way to revisit the image-processing familiarity requested.

  • Lead a technical decision

    As a preparation exercise, draft a brief decision record for integrating an AI solution into an existing system, including trade-offs and points to discuss with other teams. This mirrors the role's SME, team-decision and cross-functional responsibilities.

04

Your preparation checklist

  • Map the requirements to evidence

    The posting specifies Generative AI and LLM proficiency, at least 7.5 years of Generative AI experience, and 15 years of full-time education. Prepare a concise record of relevant work and education; note any points to clarify rather than assuming they are satisfied.

  • Build a GenAI project walkthrough

    Select one real application you helped deliver. Prepare a brief account of its objective, your decisions, the LLM or GenAI approach, integration with existing systems, and results you can substantiate.

  • Sketch a production AI pipeline

    As a practice design exercise, draw a cloud or on-premises application pipeline and explain deployment, reliability, scalability, and optimization choices. These reflect the posting’s production-ready pipeline responsibilities.

  • Rehearse technical explanations

    Prepare examples you can discuss for deep learning frameworks and neural network architectures, conversational AI or NLP, and image processing where applicable. The posting calls for depth in the first two areas and familiarity with image processing.

  • Prepare leadership decision stories

    Outline specific examples of a team-level technical decision, mentoring, and resolving a problem across teams. For each, identify your role, the trade-off, and the outcome; these match the advertised SME and collaboration responsibilities.

  • Draft questions for the employer

    Ask which AI applications and cloud or on-premises environments are in scope, how team-level decisions are shared, and whether the work location is Navi Mumbai or the Mumbai office; both locations appear in the posting.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

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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