New opportunity

AI / ML Engineer

Accenture · Hyderabad, India

About this role

Accenture seeks an AI / ML Engineer to build production-ready applications and systems using AI tools, cloud AI services, and cloud or on-premises application pipelines. The role focuses on implementing and improving generative AI models, with potential applications involving deep learning, neural networks, chatbots, image processing, and conversational AI.

Responsibilities

Work with cross-functional teams to define AI project requirements, investigate business problems, develop and test data-driven hypotheses, and identify insights for business decisions.
Implement generative AI models and optimize them for performance, scalability, and efficiency. Maintain data quality and accuracy.
Lead the design of prompt engineering strategies and apply NLP techniques to improve model outputs. Collaborate with AI researchers and developers on prompt generation approaches.
Research advances in generative AI, machine learning, and deep learning for potential use in products and services. Document technical specifications, user guides, and presentations for technical and non-technical stakeholders, and help establish generative AI development standards.

Requirements

The posting lists 5–10 years of experience and separately requires a minimum of 7.5 years in machine learning. A degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field and 15 years of full-time education are required; a Ph.D. is highly desirable.
Solid experience implementing generative AI models and understanding deep learning approaches including GPT, VAE, and GANs. Proficiency in Python and experience with TensorFlow, PyTorch, or Keras are required.
Knowledge of data structures, algorithms, software engineering principles, NLP tools such as SpaCy, NLTK, or Hugging Face, and cloud platforms such as AWS, GCP, or Azure. Familiarity with visualization tools including Matplotlib, Seaborn, or Plotly and development methods such as Agile or Scrum is required.
Experience with statistical modeling, and handling varied data formats and sources for speech and voice applications; strong analytical, problem-solving, communication, independent-working, and collaboration skills.

This full-time position is based at Accenture's Hyderabad office.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Machine LearningGenerative AIDeep LearningNeural NetworksPrompt EngineeringNatural Language ProcessingConversational AISpeech and Voice ApplicationsStatistical ModelingPythonTensorFlowPyTorchKerasGPTVariational AutoencodersGenerative Adversarial NetworksSpaCyNLTKHugging FaceAWSGoogle Cloud PlatformAzureMatplotlibSeabornPlotlyData StructuresAlgorithmsAgileScrumData Visualization

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 systems
The role develops AI applications using cloud AI services and cloud or on-premises pipelines, with production-ready quality. Prepare to explain design decisions that take a model from implementation to a dependable application.
Business-aligned, data-driven work
The posting calls for defining requirements with cross-functional teams, investigating business problems, testing hypotheses with data, and identifying decision-useful insights. Prepare an example that connects a business objective to evidence and an AI solution.
Generative AI performance and data quality
Implementing generative AI models, improving their performance, scalability and efficiency, and ensuring data quality are explicit duties. Prepare to discuss how you evaluate model changes and check the data behind them.
Prompt engineering and NLP leadership
The role leads prompt-strategy design, applies NLP techniques to improve outputs, and works with researchers and developers on prompt-generation approaches. Prepare to explain how you compare approaches and decide which outputs are better.
Technical communication and standards
The posting calls for technical specifications, user guides and presentations for varied audiences, plus contributions to generative AI development standards. Prepare to show how you communicate technical choices and make practices reusable.

Likely challenges to think through

Balancing model quality with operational demands
Likely preparation challenge, not a confirmed employer problem: improving output quality may compete with scalability and efficiency in a production pipeline. Prepare a concrete example of the trade-offs you measured and the decision you made.
Working across varied speech and voice data
Likely preparation challenge: the stated omnichannel speech and voice work involves varied data formats and sources, making consistent data quality harder. Prepare to describe validation, normalization and how you would detect inaccurate inputs.
Turning research into maintainable practice
Likely preparation challenge: the role combines tracking AI advances with production-ready delivery and development standards. Prepare to explain how you would test a promising technique before adopting it and document the resulting choice.

Practice your answers

Technical practice

How would you turn a business problem into a testable ML hypothesis?

What to coverThe role involves defining requirements with cross-functional teams, testing hypotheses with data, and identifying insights for business decisions. Suggested preparation: explain how you would choose a baseline, use statistical modeling and visualizations to assess results, and communicate what the evidence does and does not support.

How would you design a production-ready AI application pipeline?

What to coverThe posting calls for AI applications using cloud AI services and cloud or on-premises pipelines. Suggested preparation: walk through data flow, model integration, deployment, monitoring, and the trade-offs you would assess when choosing a deployment environment.

When would you use GPT, a VAE, or a GAN?

What to coverThe posting names GPT, VAEs, and GANs among the deep learning techniques relevant to the role. Suggested preparation: compare what each approach generates, its training or inference considerations, and how you would select one for a defined use case rather than by novelty alone.

How would you implement and test a generative AI model in Python?

What to coverThe role requires Python and experience with TensorFlow, PyTorch, or Keras, alongside sound algorithms and software engineering principles. Suggested preparation: describe your choices for data handling, model code, reproducible tests, and checks that the implementation behaves as intended.

How would you improve a generative model's performance, scalability, and efficiency?

What to coverOptimizing existing generative AI models across all three dimensions is an advertised responsibility. Suggested preparation: define measurable targets, locate bottlenecks, and explain trade-offs between output quality, resource use, and response time.

How would you evaluate a prompt engineering strategy?

What to coverThe role includes leading prompt strategy design and collaborating on prompt generation approaches. Suggested preparation: describe how you would build representative test cases, compare prompt variants against defined output criteria, and investigate failures before adopting a change.

How would you use NLP techniques to improve generative AI outputs?

What to coverThe posting calls for NLP techniques and names SpaCy, NLTK, and Hugging Face as examples of relevant tools. Suggested preparation: choose a concrete text-processing problem, explain the technique and tool you would consider, and show how you would measure whether it improves outputs.

What would you do if poor data quality were affecting model results?

What to coverEnsuring data quality and accuracy is an explicit responsibility. Suggested preparation: explain how you would trace the issue to its source, check formats and labels, assess its effect on results, and prevent recurrence in the pipeline.

How would you handle data from multiple channels for a speech or voice application?

What to coverThe posting specifies varied data formats and sources through omni-channel speech and voice applications within conversational AI. Suggested preparation: discuss ingestion and format consistency, how you would validate the data, and how channel differences could affect evaluation.

How would you decide whether a new generative AI technique belongs in a product?

What to coverThe role involves researching advances and identifying opportunities to integrate them into products and services. Suggested preparation: compare a new approach with an existing baseline using business fit, measured quality, operational cost, and maintainability, then explain how you would document the decision.

Behavioural practice

Tell me about aligning an ML project with a business objective.

What to coverThe role calls for working with cross-functional teams to define requirements and objectives. Suggested practice: use a real situation, your actions to resolve ambiguity or competing priorities, and the resulting decision or outcome.

Describe a time data challenged an initial hypothesis.

What to coverThe posting emphasizes testing hypotheses and deriving insights for decisions. Suggested practice: explain the situation, how you analyzed and communicated the evidence, and what changed as a result.

Tell me about collaborating with researchers or developers on an approach.

What to coverThe role explicitly involves collaborating on prompt generation approaches. Suggested practice: choose a real example, describe your contribution and how differences of opinion were handled, and state the outcome without overstating it.

Describe explaining a complex AI decision to different audiences.

What to coverThe posting calls for technical specifications, user guides, and presentations for technical and non-technical stakeholders. Suggested practice: identify the situation, how you adapted the explanation to each audience, and what the communication enabled.

Tell me about establishing or improving a development practice.

What to coverThe role includes contributing to generative AI development standards and working independently and collaboratively. Suggested practice: describe the problem, the actions you took with others to improve the practice, and the observable result.

Sharpen your knowledge

Topics to prepare

Generative model architectures
Posting: The role calls for understanding GPT, VAEs, GANs, deep learning, and neural networks. Suggested revision exercise: Compare how each architecture generates outputs, what data it needs, and one limitation relevant to choosing it for an application.
Statistical modeling and business hypotheses
Posting: The role involves investigating business problems, testing hypotheses with data, and identifying insights for decisions. Suggested revision exercise: Frame a sample business question as a testable hypothesis, then explain the data checks, statistical approach, and limits of the conclusion.
Prompt engineering and NLP principles
Posting: The role leads prompt-strategy design and applies NLP techniques to improve GenAI outputs. Suggested revision exercise: Explain how you would compare prompt approaches, define output-quality criteria, and diagnose inconsistent results.
Production AI pipeline trade-offs
Posting: Applications should be production-ready, use cloud or on-premises pipelines, and be optimized for performance, scalability, and efficiency. Suggested revision exercise: Sketch an inference pipeline and explain where you would check data accuracy, latency, and capacity.
Conversational AI and speech data
Posting: The role mentions varied data formats and sources across channels for speech and voice applications. Suggested revision exercise: Map the stages from incoming voice data to a conversational response, noting format and quality risks at each stage.

Skills to revise

Implement a model in Python
Posting: Python proficiency and experience with TensorFlow, PyTorch, or Keras are required. Suggested revision exercise: Implement a small generative-model example in Python using one listed framework, then explain its training and inference steps.
Run a prompt comparison
Posting: The role develops prompt-generation approaches and uses NLP tools such as SpaCy, NLTK, or Hugging Face. Suggested revision exercise: Compare two prompts on the same small test set, inspect recurring errors with a listed NLP tool, and record the change in results.
Validate mixed-source data
Posting: Data quality and accuracy matter, including for varied speech and voice data sources. Suggested revision exercise: Create a small sample with differing input formats; check for missing fields, inconsistent values, and records that cannot be processed.
Measure an optimization
Posting: Existing GenAI models are optimized for performance, scalability, and efficiency. Suggested revision exercise: Benchmark a small model workflow before and after one change, recording response time, resource use, and any output-quality trade-off.
Trace an application pipeline
Posting: The role uses cloud AI services and cloud or on-premises application pipelines; AWS, GCP, and Azure are named examples. Suggested revision exercise: Trace a sample request through a pipeline on a listed platform or an on-premises setup, identifying failure points and checks without assuming a particular service.
Communicate results to two audiences
Posting: The role produces technical specifications, user guides, and presentations, and names Matplotlib, Seaborn, or Plotly. Suggested revision exercise: Visualize one model result with a listed library, then write a brief technical explanation and a separate business-facing takeaway.

Your preparation checklist

Map the stated requirements to evidence
Prepare a factual evidence sheet for machine-learning experience, education, Python, a listed ML framework, NLP tooling, cloud platforms, and Agile or Scrum. Note that the posting says 5–10 years in its header but separately specifies a minimum of 7.5 years in machine learning.
Prepare a production AI project walkthrough
Choose a relevant project, if available, and explain the business problem, cross-functional requirements, data sources and quality checks, model implementation, and cloud or on-premises application pipeline. Bring a simple architecture diagram.
Rehearse a generative-model trade-off discussion
Prepare to explain when GPT, VAEs, or GANs fit a problem and how you would test performance, scalability, and efficiency. Use measured results from past work where available; otherwise distinguish your proposed tests from experience.
Work through a prompt and NLP example
Draft a short exercise showing how you would design, compare, and refine prompts and assess outputs. If relevant to your experience, include an NLP tool and the handling of varied inputs for a conversational speech or voice application.
Practice a production pipeline design exercise
Sketch a proposed AI application using cloud AI services or an on-premises pipeline. Explain data-quality checks, testing, deployment boundaries, and engineering trade-offs; present these as design choices, not as a prescribed Accenture architecture.
Prepare a business-to-technical explanation
Outline how you would turn a business problem into a testable hypothesis, use statistical analysis and a visualization to communicate findings, and document the solution for technical and non-technical stakeholders.
Write focused questions for the interviewer
Ask which applications and data types this team handles, how it evaluates generative-model and prompt changes, what development standards are in place, and how the header’s 5–10-year range relates to the stated 7.5-year machine-learning minimum.

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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