New opportunity

LLM Model Developer

Accenture · Bengaluru, India

About this role

Accenture seeks an LLM Model Developer to fine-tune large language models, particularly through instruction fine-tuning and domain adaptation, to improve their relevance and performance in specific contexts. The role also involves designing, developing and deploying enterprise-grade solutions using generative and agentic AI.

Responsibilities

Lead AI solution design and delivery to address complex business challenges, automate processes and integrate intelligent insights into enterprise workflows.
Align AI capabilities with business objectives while considering scalability, responsible use and measurable business value.

Requirements

At least 5 years of experience with large language models; the posting lists 5–10 years of experience for the role.
Strong understanding of generative and agentic AI, prompt engineering and AI evaluation frameworks.
15 years of full-time education.

This is a full-time position based at Accenture's Bengaluru office. No 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

Large Language Models (LLMs)Model fine-tuningInstruction fine-tuningDomain adaptationGenerative AIAgentic AIPrompt engineeringAI evaluationAI solution designResponsible AI

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

Instruction fine-tuning and domain adaptation
The role centers on fine-tuning LLMs to improve relevance and performance in specific contexts. Prepare to explain how you would define the target context and evaluate whether adaptation improved results.
Enterprise GenAI and agentic AI delivery
The posting calls for leading the design, development and deployment of enterprise-grade solutions that automate processes and integrate insights into workflows. Prepare to discuss how you would take a solution from a business challenge to delivery.
Prompt engineering and evaluation
The posting explicitly calls for a strong grasp of prompt engineering and AI evaluation frameworks. Prepare to explain how you would test outputs and use the findings to refine a solution.
Business value, scale and responsible use
The role must align AI capabilities with business objectives while considering scalability, responsible use and tangible value. Prepare to explain how you would define useful outcomes and account for those constraints in design decisions.

Likely challenges to think through

Improving domain relevance without losing broader performance
A likely preparation challenge is choosing how far to adapt a model for a specific context: greater relevance may come with performance trade-offs elsewhere. Prepare to describe evaluation across both the target context and broader use cases; this is a hypothesis, not a stated Accenture problem.
Balancing automation with responsible use
Automating complex processes and integrating AI into workflows may create tension between efficiency and appropriate oversight. Prepare to explain how you would decide what to automate and how you would evaluate its use; the posting does not specify particular controls.
Turning a working solution into measurable enterprise value
A solution may perform well in testing yet be difficult to scale or connect to a business objective. Prepare to distinguish model-level results from workflow outcomes and explain how you would assess both; this is a likely role challenge, not a confirmed project issue.

Practice your answers

Technical practice

How would you approach instruction fine-tuning for a specific use case?

What to coverThe role explicitly emphasizes instruction fine-tuning to improve relevance and performance. Suggested preparation: discuss training-data quality, a baseline, and how you would check whether fine-tuning improved the intended behavior.

How would you adapt an LLM to a specialized domain?

What to coverDomain adaptation is named in the role description. Suggested preparation: explain how you would identify domain-specific gaps, choose suitable adaptation data, and test for improvements without losing general usefulness.

When would you use prompt engineering rather than fine-tuning?

What to coverThe posting calls for both prompt-engineering knowledge and model fine-tuning. Suggested preparation: compare expected quality, iteration effort, maintainability, and evaluation needs rather than treating either approach as universally preferable.

How would you evaluate whether an LLM solution is fit for its intended context?

What to coverThe posting names AI evaluation frameworks and performance in specific contexts. Suggested preparation: describe task-relevant test cases, quality measures, failure analysis, and how results would inform a deployment decision.

How would you diagnose a decline in model relevance after an update?

What to coverImproving relevance and performance is central to the advertised work. Suggested preparation: compare the affected cases with a baseline and investigate changes to data, prompts, model behavior, or evaluation before choosing a remedy.

How would you design an agentic AI solution for a business task?

What to coverThe role involves designing solutions using agentic AI. Suggested preparation: explain when agentic behavior adds value, how you would bound its actions, and how you would evaluate the resulting task outcomes.

How would you integrate AI-generated insights into an enterprise workflow?

What to coverThe responsibilities explicitly include process automation and integration of intelligent insights into enterprise workflows. Suggested preparation: describe the workflow boundary, how people or systems would use the output, and how you would handle unusable results.

What would you consider when deploying an enterprise-grade LLM solution at scale?

What to coverThe posting calls for deployment of enterprise-grade solutions and attention to scalability. Suggested preparation: discuss capacity, responsiveness, reliability, and operational trade-offs without assuming a particular platform or infrastructure.

How would you account for responsible use in an LLM solution?

What to coverResponsible use is an explicit consideration in the posting. Suggested preparation: identify risks relevant to the proposed use case, explain suitable safeguards, and describe how you would assess whether they work.

How would you connect an AI solution's technical results to business value?

What to coverThe role calls for alignment with business objectives and measurable impact. Suggested preparation: define the intended outcome, establish a comparison point, and explain how you would distinguish technical improvement from useful workflow impact.

Behavioural practice

Tell me about a time you led an AI solution from design toward delivery.

What to coverThe posting assigns leadership of AI solution design and delivery. Suggested preparation: use a real situation, your decisions and actions, and the resulting outcome; distinguish your contribution from the team's.

Describe a time you aligned a technical approach with a business objective.

What to coverAligning AI capabilities with business objectives is explicitly part of the role. Suggested preparation: give the situation, explain how you established the objective and chose among trade-offs, then state the result.

Tell me about a time you addressed a model-quality problem.

What to coverThe advertised work focuses on improving model relevance and performance. Suggested preparation: use a real example to describe the observed problem, how you investigated and acted, and what the subsequent evaluation showed.

Describe a time a responsible-use concern affected an AI delivery decision.

What to coverThe posting explicitly calls for responsible use alongside delivery and scalability. Suggested preparation: explain the situation, the concern and your action, and the outcome without implying that every risk was eliminated.

Sharpen your knowledge

Topics to prepare

Instruction fine-tuning and domain adaptation
The role explicitly emphasizes both approaches. To revise, explain how they differ, when each could improve relevance in a specific context, and how you would compare the result with an untuned model.
Generative and agentic AI
The posting calls for enterprise solutions using both. To revise, describe where a generative model alone would suffice and where an agentic workflow might help automate a business process.
AI evaluation frameworks
A strong grasp of AI evaluation frameworks is stated. To revise, propose criteria for judging relevance, performance and business usefulness in a hypothetical enterprise use case.
Scalability, responsible use and business value
The posting asks for AI capabilities aligned with business objectives while considering scalability, responsible use and tangible value. To revise, work through the trade-offs you would raise before expanding a hypothetical solution from a limited trial to wider use.

Skills to revise

Plan a fine-tuning experiment
Fine-tuning LLMs is central to the role. As a preparation exercise, draft a small set of hypothetical domain-specific instruction-and-response examples, define a baseline, and specify what improved outputs would look like. This is an exercise, not a stated employer assignment.
Iterate on prompts
Prompt engineering is explicitly listed. As a preparation exercise, write and revise prompts for one hypothetical business request, then explain how you would check whether the revisions improve the response.
Evaluate model outputs
AI evaluation frameworks are explicitly listed. As a preparation exercise, create a short scoring rubric for sample outputs and use it to compare two hypothetical responses, noting what the rubric misses.
Design an enterprise AI workflow
The role involves leading solution design and delivery, automating processes, and integrating insights into enterprise workflows. As a preparation exercise, sketch a hypothetical workflow from business objective to deployment, marking where scalability, responsible use and measurable impact would be assessed.

Your preparation checklist

Map the stated requirements to evidence
Prepare a factual timeline of LLM work and a record of full-time education to discuss the posting’s minimum five years of LLM experience and 15 years of full-time education. Note any gaps rather than assuming they are met.
Build a fine-tuning project walkthrough
Prepare one relevant example covering the problem, instruction fine-tuning or domain adaptation approach, data choices, your contribution, and how you measured changes in relevance or performance.
Practice an enterprise AI design exercise
Sketch a hypothetical generative or agentic AI solution for a business workflow. Explain its components, where automation and human review belong, and how it would connect to the workflow; present it as a proposed design, not an Accenture project.
Prepare prompt and evaluation examples
Bring a prompt iteration example and an evaluation plan showing what you would test, how you would compare outputs, and how you would investigate failures. The posting specifically calls for prompt engineering and AI evaluation frameworks.
Show how you make delivery trade-offs
Prepare a leadership example that connects an AI design decision to a business objective. Be ready to explain scalability, responsible use, and how value was measured or would be measured.
Draft focused employer questions
Ask how this role balances model fine-tuning with enterprise solution delivery, what evaluation criteria matter most, and how the team defines measurable impact for its AI work.

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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