New opportunity

Large Language Model Architect

Accenture · Bengaluru, India

About this role

Accenture seeks a Large Language Model Architect for its Bengaluru office. The role combines LLM architecture with hands-on knowledge engineering: designing models that process and generate natural language, and building scalable knowledge graph and semantic solutions that integrate with clients’ broader systems. The posting requires at least 5 years of experience and 15 years of full-time education.

Responsibilities

Design and refine neural network architectures and parameters for language models trained on large volumes of unlabeled text. Evaluate model performance and align designs with project goals.
Work across the knowledge graph lifecycle, from data ingestion and modeling through curation and deployment. Design, evaluate and maintain ontologies; build knowledge graph components and support recommendations for semantic-layer solutions.
Develop AI and semantic solutions, integrate language models into wider workflows, and collaborate with users, engineers, architects, UI designers, delivery leads and client counterparts.
Act as a subject-matter expert, contribute to team decisions, guide junior engineers and share knowledge. Support client-facing work and sales efforts when called upon.

Qualifications and skills:

Google Cloud Data Services is required; the posting also specifies a minimum of 5 years’ experience with it. Microsoft Azure Data Services and Snowflake Data Warehouse are preferred.
A bachelor’s degree or equivalent is listed, along with experience in at least three of these areas: 2 years with knowledge graph technologies such as RDF, SPARQL, LPG and SHACL; 2 years in schema design, ontology management and knowledge graph curation; 2 years designing knowledge graph solutions and graph-based ML models; 1 year implementing end-to-end data pipelines for AI applications, especially LLMs; or 2 years working with relational databases, object stores, graph databases and vector databases.
The posting also calls for 2+ years with cloud platforms and 2+ years with Python; experience with TensorFlow, PyTorch and ETL tools; practical NLP or search experience; enterprise-scale prompt engineering and LLMs; team-lead and external client-facing consulting experience; and collaboration across engineering, research and product teams in multiple time zones.
Expertise in NLP neural network design, cloud-scale data processing, model tuning and evaluation, and data storage, retrieval and transformation is relevant to the role.

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

Skills for this role

Google Cloud Data ServicesSnowflake Data WarehouseMicrosoft Azure Data ServicesKnowledge GraphsRDFSPARQLLPGSHACLSchema DesignOntology ManagementKnowledge Graph CurationGraph-based Machine LearningData PipelinesRelational DatabasesObject StoresGraph DatabasesVector DatabasesStardogNeo4jAmazon NeptuneAWSAzureGoogle Cloud PlatformPythonTensorFlowPyTorchApache NiFiApache AirflowNatural Language ProcessingSearch Techniques

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

LLM Architecture and Neural Network Design
The core responsibility involves designing and refining neural network architectures and parameters for models trained on large volumes of unlabeled text. You must demonstrate the ability to evaluate model performance and align technical design choices with specific project goals and business requirements.
Knowledge Graph Lifecycle Management
You are expected to lead the end-to-end lifecycle of knowledge graphs, including ingestion, modeling, curation, and deployment. This requires proficiency in designing ontologies and building semantic-layer solutions that integrate seamlessly with a client's broader data architecture.
Cloud-Scale Data Integration
As a Google Cloud Data Services expert, you must implement scalable data pipelines for AI applications. You need to ensure that LLMs and semantic solutions are effectively integrated into enterprise workflows using cloud-native tools and diverse storage systems like vector and graph databases.
Consultative Technical Leadership
The role requires acting as a subject matter expert who guides junior engineers and collaborates with cross-functional teams, including UI designers and delivery leads. You must be prepared to articulate the value of AI solutions to clients and support sales efforts.

Likely challenges to think through

Bridging Technical Depth with Client Communication
You will face the challenge of translating complex neural network and knowledge graph architectures into business value for non-technical stakeholders. Prepare by practicing how to explain the 'why' behind your architectural choices in the context of client-specific ROI and project outcomes.
Integrating Disparate Data Ecosystems
The requirement to integrate LLMs with existing enterprise systems (relational, graph, and vector databases) presents a significant technical hurdle. Prepare by reviewing strategies for maintaining data consistency and semantic integrity when moving data across different storage paradigms in a cloud environment.
Managing Cross-Functional and Global Collaboration
Working across multiple time zones with diverse teams (research, engineering, product) requires high-level coordination. Prepare by reflecting on your experience managing technical dependencies and resolving conflicts when project requirements shift across different stakeholder groups.

Practice your answers

Technical practice

Designing Neural Network Architectures for LLMs

What to coverExplain your approach to designing neural network architectures for models trained on large volumes of unlabeled text. Discuss how you balance model complexity with computational efficiency and how you select hyperparameters to align with specific project goals.

Knowledge Graph Lifecycle Management

What to coverDescribe your experience managing the full lifecycle of a knowledge graph, from data ingestion and modeling to curation and deployment. Address how you handle schema design and ontology maintenance to ensure the graph remains scalable and relevant.

Integrating LLMs with Knowledge Graphs

What to coverHow do you approach integrating language models with semantic-layer solutions or knowledge graphs? Discuss the trade-offs between using retrieval-augmented generation (RAG) versus fine-tuning for specific enterprise knowledge retrieval tasks.

Google Cloud Data Services and Scalability

What to coverGiven the requirement for Google Cloud Data Services, describe how you have utilized these tools to build scalable data pipelines for AI applications. Focus on how you manage data storage, retrieval, and transformation at enterprise scale.

Graph Database Selection and Performance

What to coverCompare the use cases for different graph databases like Stardog, Neo4j, or Amazon Neptune. Explain how you would choose the appropriate storage technology based on the specific requirements of a graph-based machine learning project.

Evaluation Metrics for Language Models

What to coverWhat methodologies do you use to evaluate the performance of an LLM in a production environment? Discuss both quantitative metrics and qualitative assessment techniques for ensuring model output accuracy and alignment.

Implementing End-to-End Data Pipelines

What to coverDescribe your experience building end-to-end data pipelines using tools like Apache NiFi or Airflow. Explain how you ensure data quality and consistency when preparing large datasets for LLM training or knowledge graph ingestion.

Semantic Technologies (RDF, SPARQL, SHACL)

What to coverHow have you applied RDF, SPARQL, and SHACL in past projects to enforce data integrity and query complex relationships? Provide an example of how these technologies supported a broader semantic solution.

Behavioural practice

Mentoring and Team Leadership

What to coverDescribe a situation where you had to guide junior engineers or team members through a complex technical challenge. Focus on how you shared your knowledge and facilitated their professional growth while ensuring project milestones were met.

Collaborating with Cross-Functional Stakeholders

What to coverProvide an example of a project where you collaborated with non-technical stakeholders, such as UI designers or client counterparts. How did you articulate the value of your AI or knowledge graph solution to ensure alignment with their business needs?

Managing Conflicting Project Priorities

What to coverTell me about a time you had to balance technical debt or architectural refinement with urgent project delivery requirements. How did you communicate these trade-offs to your delivery leads or client counterparts?

Working in Distributed Teams

What to coverDescribe your experience working across multiple time zones with diverse engineering and research teams. What strategies did you use to maintain effective communication and ensure consistent progress on shared technical goals?

Supporting Sales and Client Engagement

What to coverShare an experience where you were involved in a sales effort or client-facing consulting role. How did you translate complex technical capabilities into a compelling recommendation that addressed the client's specific business problem?

Sharpen your knowledge

Topics to prepare

Knowledge Graph Lifecycle and Ontology Design
The role requires expertise in the full lifecycle of knowledge graphs, including schema design, curation, and maintenance. Exercise: Draft a conceptual ontology for a domain like 'Supply Chain' or 'Customer Support' and map how it would interface with a vector database for RAG (Retrieval-Augmented Generation) applications.
Neural Network Architecture for LLMs
The position involves designing and refining neural network parameters for language models. Exercise: Review the architecture of common transformer-based models and prepare to discuss how you would adjust hyperparameters or training strategies when moving from a general-purpose model to a domain-specific enterprise application.
Enterprise-Scale LLM Integration
The role focuses on integrating LLMs into broader client systems and workflows. Exercise: Outline a high-level architecture diagram showing how an LLM interacts with a semantic layer, data pipelines, and UI components to solve a specific business problem.

Skills to revise

Google Cloud Data Services
This is a mandatory requirement with a 5-year experience expectation. Exercise: Review your experience with GCP data tools (e.g., BigQuery, Dataflow, Vertex AI) and prepare to explain how you have used these services to build scalable data processing pipelines for AI projects.
Knowledge Graph Technologies (RDF, SPARQL, SHACL)
The role requires 2 years of hands-on experience with these semantic technologies. Exercise: Write a sample SPARQL query to extract specific relationships from a graph database and explain how SHACL shapes are used to validate data integrity within that graph.
Python and AI Frameworks (PyTorch/TensorFlow)
Practical coding skills are essential for model development. Exercise: Create a small script using PyTorch or TensorFlow that demonstrates a custom data loader or a model evaluation loop, ensuring it is optimized for large-scale data processing.

Your preparation checklist

Prepare a Knowledge Graph and LLM Integration Case Study
Develop a concise walkthrough of a project where you integrated a knowledge graph with an LLM. Focus on how you handled data ingestion, ontology design, and the semantic layer to improve model accuracy or retrieval-augmented generation (RAG) performance.
Demonstrate Google Cloud Data Services Proficiency
Prepare specific examples of how you have utilized Google Cloud Data Services for large-scale data processing. Be ready to discuss your experience with ETL pipelines, data storage, and how these services supported your AI or machine learning workflows.
Articulate Your Approach to Neural Network Design and Tuning
Be prepared to discuss your methodology for designing neural network architectures and tuning parameters for NLP tasks. Explain how you evaluate model performance and align technical decisions with business objectives.
Draft Examples of Cross-Functional Leadership
Prepare stories that highlight your experience leading teams and collaborating with diverse stakeholders, such as UI designers, engineers, and client counterparts, particularly in multi-time zone environments.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions regarding the team's current approach to agentic systems, how they balance knowledge engineering with generative AI, and the specific challenges they face when integrating these solutions into client systems.

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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