New opportunity

AI / ML Engineer

Accenture · Pune, India

About this role

Accenture seeks an AI / ML Engineer for its Pune office. The role focuses on developing production-ready applications and systems using AI tools, cloud AI services and generative AI models, with appropriate cloud or on-premises application pipelines. The posting lists full-time employment and 5–10 years of experience, with a minimum of 5 years required.

Responsibilities

Architect and deliver enterprise generative AI solutions, including retrieval-augmented generation (RAG) and multi-agent frameworks. Design and deploy cloud-native AI solutions on Azure, using services and practices such as Azure OpenAI, App Service, Kubernetes, AKS, Key Vault, MLOps, LLMOps, evaluation frameworks and observability.
Integrate AI technologies into existing systems, optimize performance and user experience, and design scalable AI models. Solutions may involve deep learning, neural networks, chatbots and image processing.
Serve as a subject-matter expert; collaborate with and manage the team, make team decisions, contribute to decisions across multiple teams, solve problems, mentor colleagues and evaluate new technologies and methods.

Qualifications and skills:

Required: proficiency in generative AI. The posting separately states that candidates should have at least 7.5 years of experience in generative AI and must have completed 15 years of full-time education.
Strong understanding of TensorFlow or PyTorch; experience deploying AI solutions on AWS, Azure or Google Cloud; familiarity with natural language processing; ability to design and implement scalable AI models; and good knowledge of SQL. GitLab and Ansible are also listed. No separate preferred 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

Generative AIRetrieval-Augmented Generation (RAG)Multi-agent frameworksAzure OpenAIAzure App ServiceKubernetesAzure Kubernetes Service (AKS)Azure Key VaultMLOpsLLMOpsAI evaluation frameworksAI observabilityGitLabAnsibleSQLTensorFlowPyTorchAWSMicrosoft AzureGoogle CloudNatural language processingDeep learningTeam 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

Architecting Enterprise Generative AI Solutions
The role requires designing and delivering production-ready systems using RAG and multi-agent frameworks. You must demonstrate the ability to move beyond prototyping to architecting scalable, cloud-native solutions on Azure, specifically utilizing Azure OpenAI and associated MLOps/LLMOps pipelines.
Technical Leadership and SME Oversight
As a subject matter expert, you are expected to drive decision-making across multiple teams, mentor junior staff, and evaluate emerging technologies. Success depends on your ability to balance hands-on development with the responsibility of guiding team strategy and improving overall project outcomes.
Cloud-Native AI Deployment and Observability
You must be proficient in deploying AI models within a robust infrastructure, using Kubernetes, AKS, and Azure services. A core priority is implementing observability and evaluation frameworks to ensure that deployed models remain performant, secure, and reliable in a production environment.

Likely challenges to think through

Balancing Rapid GenAI Innovation with Enterprise Stability
The fast-paced nature of generative AI creates a challenge when integrating these models into stable, existing enterprise systems. Prepare by discussing how you manage technical debt and maintain system reliability while incorporating experimental AI features that require frequent updates and monitoring.
Managing Complex Multi-Team Dependencies
The role involves contributing to decisions across multiple teams, which can lead to conflicting priorities or technical bottlenecks. Prepare by reflecting on your experience in cross-functional collaboration and how you negotiate technical trade-offs when different teams have competing requirements for AI integration.

Practice your answers

Technical practice

Architecting RAG Systems

What to coverExplain the components of a robust Retrieval-Augmented Generation pipeline. Discuss how you handle document ingestion, vector database selection, and the trade-offs between different retrieval strategies to minimize hallucinations.

Multi-Agent Frameworks

What to coverDescribe your experience designing multi-agent systems for enterprise tasks. Focus on how you manage agent communication, task delegation, and state management to ensure complex workflows are executed reliably.

LLMOps and Observability

What to coverHow do you implement observability for production LLM applications? Detail the metrics you track to monitor model performance, latency, and cost, and how you integrate these into an MLOps pipeline.

Azure Cloud-Native AI Deployment

What to coverDetail the process of deploying a scalable AI model using Azure Kubernetes Service (AKS) and Azure OpenAI. Explain how you manage secrets using Key Vault and ensure secure, production-ready deployments.

Deep Learning Framework Proficiency

What to coverCompare TensorFlow and PyTorch in the context of deploying large-scale generative models. Suggestion: Prepare to discuss why you would choose one over the other for specific production requirements like inference speed or ecosystem support.

Scalable Model Design

What to coverWhat strategies do you use to ensure AI models remain performant as user load increases? Discuss techniques like model quantization, caching, or asynchronous processing within an application pipeline.

SQL and Data Integration

What to coverHow do you integrate SQL databases with AI-driven applications? Explain how you handle data retrieval for RAG or structured data processing while maintaining performance and security.

Automation with Ansible and GitLab

What to coverDescribe how you use Ansible and GitLab CI/CD to automate the deployment of AI infrastructure. Focus on how these tools ensure consistency across development, staging, and production environments.

Behavioural practice

Technical Leadership and Decision Making

What to coverDescribe a situation where you had to make a critical technical decision that affected multiple teams. Explain how you evaluated the options, communicated the impact, and ensured alignment across stakeholders.

Mentoring and Knowledge Sharing

What to coverProvide an example of how you have mentored junior team members to improve their technical skills. Focus on the specific actions you took to foster a culture of continuous learning and the resulting impact on team performance.

Problem Solving Across Teams

What to coverTell me about a time you identified a technical bottleneck that spanned multiple teams. How did you approach the problem, collaborate with others to find a solution, and implement the fix?

Evaluating New Technologies

What to coverDescribe a time you recommended adopting a new AI technology or methodology. Explain your evaluation process, how you assessed the risks versus benefits, and how you managed the implementation within your project.

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architecture
Review the end-to-end RAG pipeline, focusing on vector database integration, document chunking strategies, and retrieval optimization. Exercise: Design a high-level architecture diagram for a RAG system that handles document updates and query latency, relevant to the requirement for delivering enterprise-grade generative AI solutions.
LLMOps and Evaluation Frameworks
Study the lifecycle of LLM applications, specifically monitoring, versioning, and evaluation metrics. Exercise: Define a set of KPIs for evaluating LLM response quality (e.g., faithfulness, relevance) and outline a strategy for continuous monitoring in production, directly addressing the need for LLMOps and observability.
Multi-Agent Frameworks
Understand the conceptual design of agentic workflows where multiple agents collaborate to solve complex tasks. Exercise: Map out a scenario where specialized agents (e.g., researcher, writer, reviewer) interact to complete a business process, relevant to the requirement for building multi-agent frameworks.

Skills to revise

Azure Cloud-Native AI Deployment
Practice deploying models using Azure OpenAI, AKS, and Key Vault. Exercise: Create a mock deployment script or configuration file that secures API keys in Key Vault and deploys a containerized model to AKS, aligning with the specific Azure-centric infrastructure requirements.
Deep Learning Framework Proficiency
Demonstrate technical depth in PyTorch or TensorFlow for model scaling. Exercise: Refactor a basic model training script to include distributed training or performance optimization techniques, relevant to the requirement for designing and implementing scalable AI models.
Infrastructure as Code (Ansible)
Use Ansible to automate environment configuration. Exercise: Write an Ansible playbook to automate the setup of a development environment or the deployment of a service, relevant to the listed requirement for GitLab and Ansible integration.

Your preparation checklist

Prepare a RAG and Multi-Agent Architecture Walkthrough
Develop a technical presentation detailing an enterprise-grade RAG system you have built. Include how you handled multi-agent orchestration, data retrieval latency, and the specific evaluation frameworks used to measure output quality and hallucinations.
Map Azure AI Services to Production Pipelines
Create a diagram showing an end-to-end deployment pipeline using Azure OpenAI, AKS, and Key Vault. Be prepared to explain how you implemented MLOps and LLMOps practices, specifically focusing on observability and model monitoring in a production environment.
Demonstrate Deep Learning Framework Proficiency
Prepare code snippets or a project summary demonstrating your experience with TensorFlow or PyTorch. Focus on how you optimized model performance for scalability and integrated these models into existing enterprise systems.
Formulate Leadership and Mentorship Examples
Prepare specific anecdotes regarding your experience as a subject matter expert. Focus on instances where you made technical decisions that impacted multiple teams, resolved complex cross-functional problems, and mentored junior engineers to improve team output.
Draft Strategic Questions for the Interviewer
Prepare questions regarding the current maturity of their LLMOps practices, how they balance on-premises versus cloud-native AI deployment requirements, and how the team evaluates new generative AI methodologies for enterprise adoption.

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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