New opportunity

AI / ML Engineer

Accenture · Hyderabad, India

About this role

Accenture is hiring a full-time AI / ML Engineer in Hyderabad, based at its HDC3C - SEZ location. The role develops production-ready AI applications and systems using AI tools and cloud AI services, with cloud or on-premises application pipelines. Large Language Models (LLMs) are the required skill, and the work includes applying generative AI models to business solutions.

Responsibilities

Design, build and deploy scalable AI applications that meet business needs and production standards.
Lead the integration of AI models, contribute to decisions across multiple teams and solve problems for the immediate team and other teams.
Act as a subject-matter expert, manage team performance, share knowledge and mentor junior colleagues.
Coordinate cross-functional work to align AI initiatives with organizational goals.

Requirements

The posting lists 5–10 years of experience and requires a minimum of 5 years of experience with LLMs.
Proficiency in LLMs; experience designing and deploying AI applications on cloud-based or on-premises infrastructure; and a strong understanding of generative AI models and their practical applications.
Familiarity with deep learning frameworks and neural network architectures, the ability to develop and optimize conversational AI systems such as chatbots, and knowledge of image processing techniques.
15 years of full-time education is required. No additional good-to-have skills are specified.

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 deploymentCloud infrastructureOn-premises infrastructureDeep learningNeural networksConversational AIChatbotsImage processingTeam leadershipMentoringCross-functional collaboration

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

Apply LLMs to business solutions
The posting names LLMs as the must-have skill and calls for practical use of generative AI models. Prepare to explain how an LLM-based application would address a business need, including how its output would be evaluated.
Deliver production-ready AI applications
The role develops and deploys scalable AI systems through cloud or on-premises pipelines. Prepare to discuss design, model integration and deployment decisions that support production quality.
Own technical and team decisions
The role expects a subject-matter expert who manages team performance, makes team decisions, shares knowledge and mentors junior colleagues. Prepare examples of technical decisions and how guidance helped a team resolve problems.
Coordinate across teams
The engineer contributes to key decisions across multiple teams and aligns AI initiatives with organizational goals. Prepare to explain how technical choices were communicated and reconciled with differing team needs.

Likely challenges to think through

Balancing model capability with production quality
A likely challenge is choosing a useful generative AI approach while still meeting the posting’s production standards. Prepare a trade-off example covering application goals, output evaluation and deployment considerations; this is a preparation hypothesis, not a stated employer problem.
Designing for different deployment environments
Because the role covers cloud and on-premises deployment, infrastructure constraints may affect integration and scalability differently. Prepare to compare how a pipeline design would change between those environments; the posting does not specify which constraints apply.
Making decisions across team boundaries
Being responsible for team decisions while contributing to decisions across multiple teams may require balancing local delivery needs with broader goals. Prepare an example of resolving a technical disagreement through clear trade-offs and cross-functional coordination; this difficulty is inferred from the duties.

Practice your answers

Technical practice

How would you turn a business need into a production-ready LLM application?

What to coverThe role calls for scalable AI applications that meet business needs and production standards. Suggested preparation: explain how you would define the task, establish success criteria, choose an application design and test it before deployment.

When would you use a generative AI model in a solution?

What to coverThe posting calls for applying generative AI models to practical business solutions. Suggested preparation: discuss where generated output adds value, when a simpler approach may suffice and how you would assess output quality.

How would you integrate an AI model into a scalable application?

What to coverLeading model integration into scalable applications is an explicit responsibility. Suggested preparation: describe the boundaries between the model and application, how you would handle demand, and what you would validate before release.

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

What to coverThe role specifies cloud or on-premises pipelines with production-ready quality. Suggested preparation: walk through development, testing, deployment and operational checks, explaining how you would detect and respond to failures.

How would you choose between cloud and on-premises deployment for an AI application?

What to coverThe posting explicitly names both deployment environments and cloud AI services. Suggested preparation: compare them against the application's needs, including capacity, operational control and integration constraints, without assuming one is preferred by the employer.

How would you evaluate and improve an LLM application's performance?

What to coverLLM proficiency is the required skill, and the role involves production-quality applications. Suggested preparation: propose task-relevant quality checks, review failure cases and explain trade-offs between output quality, response time and resource use.

How would you investigate an AI application that works in testing but fails after deployment?

What to coverThe role covers deployment and solving problems for the immediate team and other teams. Suggested preparation: explain how you would isolate the issue across application, model and infrastructure components, then verify that a fix addresses the underlying cause.

How would you develop and optimize a conversational AI system?

What to coverThe posting explicitly mentions developing and optimizing chatbots. Suggested preparation: discuss conversation flow, handling unclear requests and evaluating whether responses remain useful across different interactions.

How do neural network architecture and framework choices affect an AI solution?

What to coverFamiliarity with deep learning frameworks and neural network architectures is listed as a skill. Suggested preparation: explain how you would choose an approach for a given task and weigh capability against training or deployment complexity, without assuming a specific framework is required.

How might you integrate image processing into an AI application?

What to coverThe posting lists knowledge of image processing techniques and their integration into AI solutions. Suggested preparation: outline how you would prepare image inputs, assess the quality of the resulting output and connect that component to the wider application.

Behavioural practice

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

What to coverThe role includes responsibility for team decisions. Suggested preparation: use a real situation, explain your decision and how you involved others, then describe the result and any trade-offs.

Describe a time you aligned an AI initiative with stakeholders across teams.

What to coverThe posting calls for cross-functional coordination and contributions to key decisions across teams. Suggested preparation: identify the differing goals, the actions you took to reach alignment and the resulting decision or outcome.

Tell me about a time you helped a junior colleague solve a difficult problem.

What to coverMentorship, knowledge sharing and support for junior team members are stated responsibilities. Suggested preparation: explain the situation, how you guided rather than simply took over the work, and what changed as a result.

Describe a problem you helped resolve that affected more than one team.

What to coverThe role involves providing solutions for the immediate team and across multiple teams. Suggested preparation: describe the impact, how you coordinated the investigation or response, and the outcome.

Tell me about a time you helped a team deliver while managing competing priorities.

What to coverThe posting expects the role to act as a subject-matter expert and collaborate in managing team performance. Suggested preparation: give a real example of the priorities, the actions you took to support the team and the resulting delivery or lesson.

Sharpen your knowledge

Topics to prepare

LLMs and generative AI applications
Posting: LLM proficiency is the must-have skill, and the role applies generative AI models to business solutions. Suggested revision exercise: explain how you would choose a model for a business use case, assess its output quality, and identify limitations.
Production AI application architecture
Posting: Applications must meet production standards and use cloud or on-premises pipelines. Suggested revision exercise: sketch an application architecture and explain the steps from model integration to deployment and ongoing operation.
Deep learning and neural networks
Posting: Familiarity with deep learning frameworks and neural network architectures is listed. Suggested revision exercise: compare two neural network architectures and explain where each would fit within an AI solution.
Conversational AI and image processing
Posting: Chatbot development and optimization, plus image processing knowledge, are listed. Suggested revision exercise: outline the main design and quality considerations for a chatbot and, separately, an image-analysis component. Do not assume both are required in the same project.
Technical leadership and cross-team decisions
Posting: The role involves team decisions, mentorship, and coordination across teams. Suggested revision exercise: prepare an example of a technical trade-off, how you would explain it to other teams, and how you would guide junior colleagues through implementation.

Skills to revise

Design an LLM-enabled application
Posting: The engineer leads AI model integration into scalable applications that meet business needs. Suggested practice: draw an application flow, identify where the LLM is integrated, and explain how you would check whether the result meets the business need.
Plan a production deployment
Posting: Experience designing and deploying AI applications on cloud-based or on-premises infrastructure is listed. Suggested practice: walk through a deployment plan for either environment, including the application pipeline and checks before release.
Improve a chatbot
Posting: The role calls for the ability to develop and optimize conversational AI systems. Suggested practice: examine sample conversations, identify failure cases, propose changes, and describe how you would compare results before and after.
Integrate image analysis
Posting: Knowledge of image processing techniques and their integration into AI solutions is listed. Suggested practice: sketch how an image input would be processed, analyzed, and passed to an application; explain how you would check the output.
Lead a technical review
Posting: The engineer is expected to contribute to decisions across teams and mentor junior members. Suggested practice: rehearse presenting an AI integration decision, its trade-offs, and actionable feedback for a junior engineer.

Your preparation checklist

Map the stated requirements to evidence
Prepare a one-page record of LLM experience, cloud or on-premises AI deployments, and 15 years of full-time education. The posting specifies at least five years of LLM experience; use dates and concrete work examples rather than unsupported claims.
Rehearse a production AI project walkthrough
Choose one application you worked on and explain the business need, your LLM or generative AI choices, integration into the application pipeline, deployment environment, production-readiness work, and your own decisions. These topics reflect the advertised responsibilities.
Practice a scalable application design
As interview preparation, sketch a cloud or on-premises pipeline that integrates a generative AI model into an application. Be ready to explain scalability, deployment, and how the design serves a business need; this is a suggested exercise, not a stated interview task.
Prepare examples across the listed technical areas
Have concise examples ready for deep learning frameworks and neural networks, conversational AI optimization, and image processing where you have relevant work to discuss. The posting lists these areas alongside its required LLM proficiency.
Document leadership and cross-team decisions
Prepare two specific accounts of a team decision or difficult problem and of mentoring or knowledge sharing. Explain how you worked with other teams and aligned an AI initiative with organizational goals, as the role describes.
Draft questions for the employer
Ask which AI applications and deployment environments this team owns, how production standards are assessed, and where the role has decision-making and mentoring responsibility. These questions clarify responsibilities stated in the posting without assuming a particular project or process.

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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