New opportunity

Large Language Model Architect

Accenture · Hyderabad, India

About this role

Accenture seeks a Large Language Model Architect for its Hyderabad office. The role designs and refines language models that process and generate natural language, while helping enterprise clients build and operationalize production-ready AI and machine learning solutions.

Responsibilities

Lead the design, training, evaluation and optimization of language-model and neural-network architectures using large text datasets. Align model capabilities with business objectives and technical requirements.
Translate client problems into scalable AI solutions; select and customize models, including deep learning and generative AI, and integrate cloud or third-party AI services where appropriate.
Build data pipelines and storage and retrieval mechanisms for training and production. Address data quality, deployment, monitoring and reliability through DevOps, MLOps and LLMOps practices.
Research AI and high-performance computing approaches, justify model choices, resolve training and deployment challenges, and collaborate with business and technical teams to deliver production systems.
Serve as a subject-matter expert, make team decisions, coordinate across teams, and mentor colleagues.

Requirements

Proficiency in Databricks Unified Data Analytics Platform. The posting specifies a minimum of 7.5 years of experience with this platform.
The listing states 5–10 years of experience. It also specifies at least 5 years as a machine learning engineer or scientist deploying models at scale, including monitoring, alerting, automatic bug filing and auditing; 5 years applying computer science foundations; 3 years in distributed computing; and 2 years building and deploying AI/ML software in the cloud.
Proficiency in Python and Python-based AI/ML frameworks; experience with language-model APIs and their application-specific optimization; experience with Python, C++, Java, R and SQL. Strong communication, consulting and cross-functional collaboration skills are required.
A bachelor's degree or equivalent and 15 years of full-time education are specified. An MS or PhD in a related field is preferred. Machine learning and data science are listed as good-to-have skills.

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

Skills for this role

Large Language ModelsNatural Language ProcessingNeural Network ArchitectureDatabricks Unified Data Analytics PlatformMachine LearningData ScienceDeep LearningGenerative AIMLOpsLLMOpsData PipelinesData PreprocessingDistributed ComputingCloud ComputingHigh-Performance ComputingPythonTensorFlowPyTorchC++JavaRSQLModel TrainingModel DeploymentStakeholder CommunicationTeam Leadership

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

Architect and refine language models
The role leads language-model architecture, training, evaluation and optimization using large text datasets, with model capabilities aligned to project goals.
Turn client needs into production AI
The posting emphasizes assessing client needs and data, choosing or customizing models and AI services, and justifying approaches in business terms.
Build reliable data and model operations
Responsibilities span scalable data pipelines, storage and retrieval, deployment, and MLOps/LLMOps. The posting specifically names production monitoring, alerting, automatic bug filing and auditing.
Apply Databricks and distributed systems expertise
Databricks Unified Data Analytics Platform is the stated must-have skill; the role also calls for large-scale data processing, distributed computing and cloud deployment.
Lead across technical and business teams
The architect is expected to make team decisions, coordinate with client-side and internal stakeholders, explain technical choices, and mentor colleagues.

Likely challenges to think through

Balance model quality against practical constraints
Preparation hypothesis: improving an LLM may compete with data availability, compute and infrastructure limits. Prepare a concise example of how you would compare approaches, evaluate results and justify a feasible choice.
Choose what to build versus integrate
Preparation hypothesis: custom models, language-model APIs and cloud or third-party services can offer different levels of control and implementation effort. Prepare to explain decision criteria tied to a client's use case and technical requirements.
Carry reliability through deployment
Preparation hypothesis: a model that evaluates well before launch may still be difficult to operate at scale. Prepare to describe how data quality, monitoring, alerting and auditing would inform troubleshooting and refinement.

Practice your answers

Technical practice

How would you design an LLM architecture for a client's natural-language task?

What to coverSuggested preparation: Explain how the task, available text data, performance goals and infrastructure constraints shape architecture and training choices. Describe how you would refine the model when evaluation shows a gap against the business objective.

How would you use Databricks to support large-scale model data pipelines?

What to coverSuggested preparation: Walk through preprocessing, data-quality checks and efficient movement of large datasets into training and production workflows. Explain how you would assess pipeline scalability and troubleshoot poor-quality inputs.

How would you evaluate an LLM before recommending it for production?

What to coverSuggested preparation: Connect evaluation criteria to the client's use case, model quality and operational reliability rather than relying on a single score. Explain how results would inform model refinement and a recommendation to stakeholders.

When would you customize a model rather than use a language-model API or cloud AI service?

What to coverSuggested preparation: Compare the approaches against business needs, available data, application-specific performance and system constraints. Explain what evidence would justify the selected approach and its value to the client.

How would you design storage and retrieval for model data and knowledge?

What to coverSuggested preparation: Describe how training and production needs affect storage, retrieval efficiency and data quality. Discuss how you would test whether the design remains practical as datasets and usage grow.

How would you operationalize monitoring for a deployed model at scale?

What to coverSuggested preparation: Cover monitoring, alerting, automatic bug filing and auditing, all of which appear in the posting. Explain how signals would lead to investigation, resolution and continued assessment of reliability.

How would you troubleshoot a model-training problem?

What to coverSuggested preparation: Work systematically through input data, preprocessing, neural-network configuration, training behavior and compute constraints. Explain what evidence would distinguish a data issue from an architecture or implementation issue.

How would distributed computing influence your model-training design?

What to coverSuggested preparation: Discuss when dataset size or computation warrants distributed or high-performance computing, and what complexity that introduces. Explain how you would weigh training efficiency against infrastructure and operational requirements.

How would you take an AI/ML solution from development into a cloud production environment?

What to coverSuggested preparation: Describe the transition from model and data pipelines to deployment, integration, monitoring and reliability. Explain how you would coordinate technical interfaces and resolve deployment issues across teams.

How would you choose a Python-based framework for an NLP model implementation?

What to coverSuggested preparation: Compare framework suitability for the intended neural architecture, training workflow and production integration without assuming a mandated framework. The posting names Python-based frameworks and cites TensorFlow and PyTorch as examples.

Behavioural practice

Tell me about a time you aligned a technical model decision with a business objective.

What to coverSuggested preparation: Use a real situation, the stakeholders and constraints involved, your actions to explain the trade-offs, and the result. Focus on how you established both business value and technical feasibility.

Describe a difficult technical decision you made while coordinating multiple teams.

What to coverSuggested preparation: Set out the situation, the competing views, your decision-making actions and the result. Clarify your own role and how you handled dependencies between teams.

Tell me about a time you resolved a production model or pipeline issue.

What to coverSuggested preparation: Describe the situation and observable problem, how you investigated and communicated it, and the result. Where relevant to your real example, explain what monitoring or follow-up changed.

Give an example of mentoring a colleague on a complex technical topic.

What to coverSuggested preparation: Explain the situation, what the colleague needed, the actions you took to share knowledge, and the result. Focus on how you adapted your explanation and supported the team's work.

Describe a time you explained an AI approach to a non-technical client stakeholder.

What to coverSuggested preparation: Identify the situation, the stakeholder's decision or concern, how you communicated the approach and trade-offs, and the result. Show how you checked that the explanation addressed the client's needs.

Sharpen your knowledge

Topics to prepare

Databricks platform architecture
Draw how large text datasets would move through Databricks for preprocessing, model training, and production use. This is a revision exercise, not a prescribed design; Databricks Unified Data Analytics Platform is the posting’s must-have skill.
LLM architecture and evaluation
Compare two neural-network design choices for a natural-language task, including how you would evaluate and refine them. The role explicitly leads language-model design, training, evaluation, and optimization.
Distributed and cloud computing
Explain how dataset size, compute distribution, and cloud deployment constraints would affect an AI/ML design. The posting specifies distributed-computing and cloud-deployment experience and mentions high-performance computing.
Production MLOps and LLMOps
Map the stages from training data to deployment, monitoring, alerting, automatic bug filing, and auditing. These production responsibilities are explicitly listed.
Client-driven model selection
Take a hypothetical client problem and compare a customized model with a cloud or third-party AI service against business needs, available data, and infrastructure constraints. This is a suggested practice scenario based on the stated consulting and solution-design duties.

Skills to revise

Build a Databricks data pipeline
If you have access, practice preprocessing sample text and preparing it for training in Databricks; otherwise, write an implementation plan covering data quality, storage, and retrieval. The posting requires Databricks proficiency and large-scale data pipelines.
Train and troubleshoot with Python
Use Python and a Python-based AI/ML framework to run a small language-model training or refinement exercise; record an evaluation result and diagnose one training issue. Python, relevant frameworks, and model-training troubleshooting appear in the posting.
Optimize language-model API use
Prototype an application-specific API workflow and compare two configurations using quality, latency, and usage measurements. The comparison is suggested practice; experience optimizing language-model APIs for specific applications is explicitly requested.
Design deployment safeguards
Draft a deployment checklist with a monitored failure condition, an alert, an automatically created bug record, and an audit trail. The posting explicitly calls for production deployment at scale with all four operational elements.
Explain and defend an architecture decision
Prepare a short client-facing explanation of a model choice, its business value, and its technical limitations; then rehearse answering questions from both business and engineering perspectives. Communication, consulting, cross-functional decisions, and mentorship are stated responsibilities.

Your preparation checklist

Map the stated requirements to evidence
Prepare a one-page evidence sheet for Databricks experience, production ML deployment, computer science foundations, distributed computing, cloud AI/ML deployment, programming languages and education. Record dates and specific examples without overstating experience.
Prepare an end-to-end project walkthrough
Choose an actual AI/ML project, if available, and outline the client problem, data, model choice, deployment, measurable results and your decisions. The role calls for production-ready solutions that connect technical work to business needs.
Rehearse an LLM architecture design exercise
As interview preparation, sketch a solution for a hypothetical enterprise language task. Explain when you would customize a model or use an LLM API, how you would evaluate it, and the trade-offs among quality, scale, infrastructure and business value.
Diagram the data and Databricks workflow
Prepare a diagram showing preprocessing of large text datasets, storage and retrieval, training inputs and production data flow. Identify where Databricks fits, drawing on work you can substantiate; platform proficiency is the posting’s must-have skill.
Document production operations and troubleshooting
Prepare a deployment example or proposed runbook covering monitoring, alerting, automatic bug filing, auditing, reliability and a training or deployment failure you resolved. These practices are explicitly named in the posting.
Practice a stakeholder and team-decision account
Prepare a concise example of explaining a model trade-off to non-technical stakeholders and coordinating a decision across teams. Include any mentorship or knowledge-sharing contribution you can substantiate; the role expects an SME who helps lead team decisions.
Bring targeted questions for the employer
Ask about the intended balance between LLM architecture and broader client AI/ML delivery, the Databricks and cloud environment, how production quality is measured, and how the stated 7.5-year Databricks minimum relates to the 5–10-year experience label.

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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