New opportunity

AI / ML Engineer

Accenture · Mumbai, India

About this role

Accenture is hiring a full-time AI / ML Engineer for its M2B - STPI location in Mumbai. The role develops production-ready AI applications and systems using cloud AI services or on-premises infrastructure. Proficiency in Large Language Models (LLMs) is required, and the engineer will apply generative AI models as part of technical solutions.

Responsibilities

Lead the design and implementation of scalable AI solutions and application pipelines that meet project requirements and quality standards.
Serve as a subject-matter expert, manage and mentor the team, make team decisions, and solve problems across multiple teams.
Work with stakeholders to gather requirements, translate them into technical specifications, and align solutions with project goals.
Monitor and optimize AI model and system performance in production. Depending on the solution, work may include deep learning, neural networks, chatbots, or image processing.

Requirements

At least 5 years of experience; the posting specifies a 5–10-year experience range and a minimum of 5 years of experience with LLMs.
Proficiency in LLMs; experience developing and deploying AI applications with cloud AI services and on-premises infrastructure; and understanding of generative AI techniques and applications.
Familiarity with deep learning frameworks and neural network architectures, plus the ability to build maintainable production AI pipelines and integrate models into data-processing and application workflows.
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 servicesAI application developmentAI model deploymentAI pipelinesDeep LearningNeural NetworksImage ProcessingChatbotsModel performance optimizationTeam leadershipStakeholder 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 solution design
The role leads the design and implementation of scalable AI applications and cloud or on-premises pipelines that meet project requirements and quality standards. Prepare to explain architectural decisions and how a solution becomes maintainable in production.
Practical LLM and generative AI application
LLMs are the required skill, and the role applies generative AI models in technical solutions. Prepare a concrete account of how model choice, application integration, and evaluation supported a use case.
Technical leadership across teams
The posting expects an SME who manages and mentors a team, makes team decisions, and helps solve problems across multiple teams. Prepare examples of decisions, knowledge sharing, and resolving technical disagreements.
Stakeholder alignment and production performance
The engineer gathers stakeholder requirements, translates them into technical specifications, and monitors and optimizes deployed models and systems. Prepare to show how requirements shaped measurable technical choices and later improvements.

Likely challenges to think through

Balancing model quality with production constraints
Preparation hypothesis, not a stated employer problem: an LLM solution may perform well in development yet be difficult to run reliably at scale. Prepare to discuss how you would evaluate quality alongside operational constraints and decide what to optimize.
Choosing a workable deployment pipeline
Preparation hypothesis, not a stated employer problem: supporting cloud AI services or on-premises infrastructure may involve different integration and maintenance trade-offs. Prepare to explain the factors you would use to choose a pipeline for given requirements.
Turning varied input into a team decision
Preparation hypothesis, not a stated employer problem: stakeholder goals and multiple teams' technical views may not initially align. Prepare an example of converting ambiguous requirements into specifications while making and communicating a defensible decision.

Practice your answers

Technical practice

How would you choose a generative AI approach for a project's requirements?

What to coverThe role requires LLM proficiency and practical use of generative AI models. Suggested preparation: explain how you would compare approaches against the intended task, output quality, operational constraints and maintainability.

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

What to coverThe posting calls for experience developing and deploying AI applications with both cloud AI services and on-premises infrastructure. Suggested preparation: discuss how project requirements and production constraints would shape the choice and the application pipeline.

How would you design a scalable, maintainable production AI pipeline?

What to coverThe role leads AI solution design and requires production-ready, scalable pipelines. Suggested preparation: describe pipeline components, their interfaces and how you would make changes without disrupting the application.

How would you integrate an AI model into data-processing and application workflows?

What to coverThe posting explicitly calls for integrating models with data-processing and application workflows. Suggested preparation: trace inputs, model execution and outputs, including how you would handle integration failures.

How would you evaluate whether an AI solution meets project quality standards?

What to coverThe engineer leads solutions that must meet project requirements and quality standards. Suggested preparation: explain how you would turn those requirements into checks for model outputs and end-to-end application behaviour.

How would you investigate and improve an AI system performing poorly in production?

What to coverMonitoring and optimizing model and system performance in production are stated responsibilities. Suggested preparation: explain how you would distinguish a model issue from a pipeline or application issue, then assess whether a change improved performance.

How would neural network architecture affect an AI solution design?

What to coverThe posting asks for familiarity with deep learning frameworks and neural network architectures. Suggested preparation: discuss how an architecture's capabilities and operational demands would affect a practical implementation, without assuming a particular framework is required.

How would you design a chatbot using a generative AI model?

What to coverChatbots are listed as a possible area of work, not a requirement for every project. Suggested preparation: explain how you would handle conversational context, assess response quality and connect the model to an application workflow.

How would you approach an AI application involving image processing?

What to coverImage processing is another possible area named in the role description. Suggested preparation: outline how you would define the image task, select an appropriate model approach and evaluate its outputs within a production pipeline.

Behavioural practice

Tell me about a time you translated stakeholder needs into technical specifications.

What to coverGathering requirements and aligning solutions with project goals are stated responsibilities. Suggested preparation: use situation, action and result to show how you resolved ambiguity and confirmed the specifications met the need.

Describe a consequential technical decision you made while leading a team.

What to coverThe role is responsible for team decisions and leading AI solution design. Suggested preparation: use situation, action and result to explain the options, your reasoning and the effect of the decision.

Tell me about a problem you solved across multiple teams.

What to coverThe posting expects engagement on key decisions and problem-solving beyond the immediate team. Suggested preparation: use situation, action and result to show how you coordinated people with different responsibilities and reached a resolution.

Describe how you helped a teammate develop their technical understanding.

What to coverKnowledge sharing and mentorship are explicit responsibilities. Suggested preparation: use situation, action and result to show what support you provided and what changed as a result.

Tell me about a time you led delivery while balancing requirements and quality.

What to coverThe role leads implementation of AI solutions that meet project requirements and quality standards. Suggested preparation: use situation, action and result to explain the trade-off, how you guided the work and how you judged the outcome.

Sharpen your knowledge

Topics to prepare

LLMs and generative AI
Required: LLM proficiency and understanding of generative AI applications. Revise by explaining when an LLM fits a proposed application, its limitations, and how you would assess whether its outputs meet the project's needs.
Cloud versus on-premises architecture
The role develops AI applications using cloud AI services or on-premises infrastructure. Sketch both architectures for the same application and explain the deployment and operational trade-offs.
Production AI pipeline design
The posting calls for scalable, maintainable pipelines that integrate models with data-processing and application workflows. Trace data and requests through a proposed system, then identify where scale, reliability, and maintainability could become concerns.
Deep learning and neural networks
Familiarity with deep learning frameworks and neural network architectures is listed; chatbots and image processing are possible work, not guaranteed assignments. Revise the architectural concepts you would use to explain a relevant model choice and its limitations.

Skills to revise

Translate requirements into a technical specification
Practice turning a short stakeholder request into a scoped AI solution: record assumptions, application requirements, model role, pipeline boundaries, and acceptance criteria. This reflects the stated stakeholder coordination and design responsibilities.
Design a model-to-application integration
Draw a concrete data-processing and application workflow around an LLM, including inputs, model interaction, outputs, and failure handling. Use it to rehearse how you would make the solution production-ready, as the posting requires.
Diagnose production performance
Work through a hypothetical case where an AI system becomes slower or produces poorer results: choose measurements, isolate likely causes, and propose an optimization you would verify. This practices the stated monitoring and optimization responsibility.
Lead a cross-team technical decision
Rehearse presenting two viable solution designs, their trade-offs, and a decision to stakeholders; then explain how you would share the rationale with and mentor the team. This matches the advertised SME, team-decision, and cross-team responsibilities.

Your preparation checklist

Map your experience to the stated requirements
Prepare a dated summary of LLM work, AI application development and deployment, and education. The posting specifies at least five years of LLM experience and 15 years of full-time education; do not assume how Accenture will verify them.
Prepare a production AI project walkthrough
Choose a relevant example and explain the problem, your decisions, how a generative AI model fit the solution, and how the application reached production-ready quality. Be precise about your own contribution.
Practice a scalable pipeline design
Sketch an AI application pipeline from data processing through model integration and deployment. Explain maintainability, scaling, and trade-offs between cloud AI services and on-premises infrastructure, both named in the posting.
Show how you evaluate production performance
Prepare an example of monitoring an AI model or system, identifying a performance issue, and deciding what to optimize. If relevant to your work, explain how you assessed output quality.
Document leadership and cross-team decisions
Select an example that shows how you made a technical decision, addressed a problem across teams, or shared knowledge with colleagues. These reflect the role’s SME, team-decision, and mentorship responsibilities.
Rehearse stakeholder-to-specification translation
Prepare a short example of turning stakeholder requirements into technical specifications, including how you clarified goals and resolved a trade-off.
Write targeted questions for the employer
Ask which AI use cases and deployment environments this position will focus on, how production model performance is assessed, and what decisions the engineer would own across teams.

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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