New opportunity

AI / ML Engineer

Accenture · Bengaluru, India

About this role

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

Responsibilities

Design, implement and maintain AI solutions using cloud or on-premises application pipelines, with attention to quality, performance and scalability.
Work independently, develop subject-matter expertise and contribute solutions in team discussions.
Collaborate with cross-functional teams to gather requirements and translate them into technical specifications.
Test and validate AI models for accuracy and reliability; enhance chatbot and image-processing capabilities as needed.

Requirements and qualifications:

Proficiency in large language models is required. The posting specifies a minimum of 3 years of experience, including at least 3 years with LLMs; its listing header also states an experience range of 2–5 years.
Strong understanding of deep learning frameworks such as TensorFlow or PyTorch, experience deploying AI models on AWS, Azure or Google Cloud, and familiarity with data preprocessing and data pipeline development.
Fifteen years of full-time education is required.

Preferred skills: Prompt engineering and generative AI experience are listed as good to have. This is a full-time position based at the Bengaluru office.

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)Prompt EngineeringGenerative AIDeep LearningNeural NetworksChatbotsImage ProcessingTensorFlowPyTorchAWSAzureGoogle CloudData PreprocessingData Pipeline DevelopmentCloud AI ServicesAI Model Testing

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 LLM Integration
The role centers on developing applications that utilize Large Language Models and Generative AI. You must demonstrate the ability to move beyond experimental code to build scalable, high-performance, and reliable production pipelines using cloud AI services.
Cross-Functional Technical Translation
You will be responsible for gathering requirements from non-technical stakeholders and translating them into precise technical specifications. Success requires balancing business needs with the technical constraints of deep learning and neural network architectures.
Model Validation and Reliability
A core duty is the rigorous testing and validation of AI models. You must establish systematic approaches to ensure accuracy, particularly for chatbot and image-processing features, to maintain quality standards in a production environment.

Likely challenges to think through

Balancing Independent Expertise with Team Collaboration
The role requires both independent subject-matter expertise and active participation in team discussions. Prepare to demonstrate how you maintain deep technical focus while effectively contributing to collaborative problem-solving sessions.
Scalability in Cloud Environments
Deploying models on AWS, Azure, or Google Cloud involves managing infrastructure costs and latency. Be prepared to discuss how you optimize data pipelines and model inference to ensure scalability when transitioning from development to production.

Practice your answers

Technical practice

LLM Deployment Strategies

What to coverDiscuss the trade-offs between using pre-trained LLMs via APIs versus fine-tuning and hosting custom models. Focus on considerations for latency, cost, and data privacy when deploying these models in a production environment.

Deep Learning Framework Selection

What to coverCompare the strengths of TensorFlow and PyTorch for building and deploying neural networks. Explain how you would choose between them based on project requirements like research flexibility versus production-ready deployment pipelines.

Data Pipeline Architecture

What to coverDescribe the components of a robust data pipeline for training and inference in AI applications. Address how you handle data preprocessing, versioning, and ensuring data quality before it reaches the model.

Cloud AI Service Integration

What to coverExplain how you would architect an AI solution using cloud-native services from AWS, Azure, or Google Cloud. Focus on how to manage scalability and performance when integrating these services into an existing application pipeline.

Model Validation and Testing

What to coverDetail your methodology for validating AI models to ensure accuracy and reliability. Suggest how you would implement automated testing for model performance metrics and handle potential drift in production.

Prompt Engineering Techniques

What to coverExplain how prompt engineering can be used to improve the output quality of generative AI models. Provide examples of techniques like few-shot prompting or chain-of-thought to guide model behavior effectively.

Image Processing and Neural Networks

What to coverDescribe how you would approach an image processing task using deep learning. Discuss the selection of architectures, such as CNNs or Vision Transformers, and the importance of preprocessing for image data.

Chatbot Optimization

What to coverDiscuss strategies for enhancing chatbot performance, specifically regarding context management and response relevance. Suggest how to evaluate user satisfaction and model accuracy in a conversational AI system.

Behavioural practice

Translating Technical Requirements

What to coverDescribe a time you collaborated with cross-functional teams to gather requirements. Explain how you translated business needs into specific technical specifications for an AI project.

Independent Problem Solving

What to coverProvide an example of a complex technical challenge you faced while working independently. Detail the steps you took to research the issue, develop a solution, and validate your approach.

Contributing to Team Discussions

What to coverShare an experience where you contributed a solution during a team discussion. Focus on how you communicated your technical reasoning and how you handled feedback from other team members.

Managing Project Quality

What to coverDescribe a situation where you had to balance the need for rapid development with the requirement for production-ready quality. Explain how you ensured the final output was scalable and reliable.

Sharpen your knowledge

Topics to prepare

Production-Ready AI Pipelines
Review the lifecycle of deploying AI models from development to production. Exercise: Design a high-level architecture diagram for a scalable chatbot service, including data ingestion, model inference, and monitoring components. This is relevant to the requirement for building production-ready applications with scalability and performance.
Deep Learning Model Validation
Study methodologies for testing and validating neural networks for accuracy and reliability. Exercise: Define a testing strategy for an image processing model, focusing on edge cases and performance metrics. This directly addresses the responsibility to conduct thorough testing and validation of AI models.

Skills to revise

Large Language Model (LLM) Integration
Practice implementing LLMs within application frameworks. Exercise: Build a simple RAG (Retrieval-Augmented Generation) pipeline using a framework like LangChain to query a local document set. This is a core requirement for the role and essential for the requested LLM proficiency.
Cloud AI Deployment
Gain hands-on experience with cloud-native AI services on AWS, Azure, or GCP. Exercise: Create a containerized model inference service and deploy it to a cloud provider's managed service (e.g., AWS SageMaker or Azure Machine Learning). This aligns with the requirement for deploying AI models in cloud environments.
Prompt Engineering
Develop techniques for optimizing model outputs for specific tasks. Exercise: Create a set of system prompts and few-shot examples to improve the accuracy of a chatbot in a specific domain. This is listed as a preferred skill and is critical for generative AI applications.
Data Pipeline Development
Practice cleaning and preparing data for model training or inference. Exercise: Write a script to automate the preprocessing of unstructured text data into a format suitable for fine-tuning or RAG. This is a foundational skill for the required data pipeline development responsibilities.

Your preparation checklist

Prepare a technical case study on LLM deployment
Develop a concise walkthrough of a project where you deployed an LLM-based application. Focus on the end-to-end pipeline, including data preprocessing, model selection, and the specific cloud infrastructure (AWS, Azure, or GCP) used to ensure scalability and performance.
Demonstrate proficiency in deep learning frameworks
Create a portfolio snippet or code repository demonstrating your ability to build and test neural networks using either TensorFlow or PyTorch. Include evidence of how you validated model accuracy and reliability in a production-like environment.
Develop a prompt engineering and chatbot optimization strategy
Prepare a summary of your experience with prompt engineering techniques used to improve chatbot performance. Be ready to discuss how you iterate on prompts to enhance model output quality and handle edge cases in generative AI applications.
Formulate questions regarding production-readiness at Accenture
Prepare thoughtful questions for the interviewers about their current AI development lifecycle, such as how they manage data pipelines for generative AI, their approach to model monitoring in production, and how they balance performance with scalability in their cloud-based AI solutions.

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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