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 neural networks that process and generate natural language and building scalable knowledge graph and semantic solutions that integrate with clients’ broader systems.

Responsibilities

Design, evaluate and refine language model architectures, parameters and performance; develop AI and knowledge graph components from data ingestion and modeling through curation and deployment.
Design and maintain ontologies, support semantic-layer recommendations, and help explain the business value of generative AI and knowledge graphs.
Work with users, client counterparts, engineers, architects, researchers, product teams and UI designers. Contribute to team decisions, mentor junior engineers, share technical knowledge and support sales efforts when requested.

Qualifications and skills:

A minimum of 5 years’ experience is required, including a stated minimum of 5 years with Google Cloud Data Services, the must-have skill. The posting specifies 15 years of full-time education and a bachelor’s degree or equivalent.
The posting asks for at least three of these areas: 2 years with knowledge graph technologies such as RDF, SPARQL, LPG or SHACL; 2 years in schema design, ontology management and graph curation; 2 years designing knowledge graph solutions and graph-based ML models; 1 year implementing end-to-end AI data pipelines, particularly for LLMs; or 2 years with relational, object, graph and vector data stores.
It also lists 2+ years of hands-on cloud platform experience and 2+ years in Python, with exposure to TensorFlow, PyTorch and ETL tools such as Apache NiFi or Airflow. Practical NLP or search experience, enterprise prompt engineering and LLM work, collaboration, team leadership and client-facing consulting experience are listed.
Microsoft Azure Data Services and Snowflake Data Warehouse are 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

Google Cloud Data ServicesMicrosoft Azure Data ServicesSnowflake Data WarehouseLarge language modelsKnowledge graphsRDFSPARQLLPGSHACLOntology managementSchema designGraph-based machine learningData pipelinesRelational databasesObject storesGraph databasesVector databasesStardogNeo4jAmazon NeptuneAWSAzureGoogle Cloud PlatformPythonTensorFlowPyTorchApache NiFiApache AirflowNatural language processingSearch techniques resume?

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

Google Cloud Data Services Mastery
The role explicitly mandates a minimum of 5 years of experience with Google Cloud Data Services. You must be prepared to demonstrate deep expertise in leveraging GCP's ecosystem for data ingestion, storage, and pipeline management to support LLM and knowledge graph workloads.
Knowledge Graph and Semantic Integration
You are expected to lead the full lifecycle of knowledge graph development, including ontology design, schema management, and curation. Success depends on your ability to integrate these semantic layers with broader client systems and articulate their business value.
End-to-End LLM Pipeline Architecture
The position requires designing and implementing scalable AI pipelines. You must be able to bridge the gap between raw data ingestion and model deployment, ensuring that neural network architectures and parameter tuning are optimized for enterprise-scale natural language processing.
Consultative Leadership and Mentorship
As a team lead and SME, you must balance hands-on technical contributions with the ability to mentor junior engineers and support sales efforts. You will be evaluated on your capacity to collaborate across cross-functional teams and manage client-facing relationships effectively.

Likely challenges to think through

Translating Technical Complexity to Business Value
A significant challenge is explaining the ROI of generative AI and knowledge graph solutions to non-technical stakeholders. Prepare by practicing how to map complex architectural decisions—such as ontology choices or model parameter tuning—to specific client business problems.
Managing Diverse Stakeholder Expectations
The role requires working with users, researchers, product teams, and UI designers across different time zones. Prepare for this by highlighting your experience in facilitating consensus during technical decision-making and managing competing priorities in a fast-paced consulting environment.
Balancing Individual Contribution with Team Leadership
Transitioning from a strong individual contributor to a team lead involves delegating tasks while maintaining high technical standards. Be ready to discuss your approach to code reviews, technical mentorship, and how you ensure team decisions align with project goals.

Practice your answers

Technical practice

Google Cloud Data Services Architecture

What to coverExplain how you have utilized Google Cloud Data Services to support large-scale AI or data engineering projects. Focus on your experience with specific services like BigQuery, Cloud Storage, or Dataflow, and how they integrate into an end-to-end LLM data pipeline.

Knowledge Graph and Ontology Design

What to coverDescribe your process for designing and maintaining ontologies for enterprise applications. Discuss how you select between RDF/SPARQL and Labeled Property Graphs (LPG) based on specific project requirements and data complexity.

LLM Data Pipeline Implementation

What to coverDetail your experience building end-to-end data pipelines for LLMs, specifically regarding data ingestion, curation, and vectorization. Address how you handle data quality and transformation using tools like Apache NiFi or Airflow.

Graph-based Machine Learning

What to coverExplain how you integrate graph-based ML models with LLMs to improve context or reasoning. Discuss the trade-offs between using graph databases like Stardog or Neo4j versus traditional relational databases for these specific use cases.

Neural Network Parameter Tuning

What to coverDiscuss your approach to evaluating and refining neural network architectures for natural language processing. Mention specific metrics you use to measure performance and how you balance model complexity with inference latency.

Enterprise Prompt Engineering

What to coverHow do you approach prompt engineering for enterprise-scale applications to ensure consistency and reliability? Suggestion: Discuss techniques like few-shot prompting, chain-of-thought, or retrieval-augmented generation (RAG) to ground model outputs.

Semantic Layer Recommendations

What to coverHow do you articulate the business value of a semantic layer to non-technical stakeholders? Focus on how you translate complex graph-based or AI-driven insights into actionable business outcomes.

Vector Database Integration

What to coverCompare the use of vector databases with traditional relational or object stores in the context of LLM applications. Explain how you manage data synchronization and retrieval efficiency in a production environment.

Behavioural practice

Mentoring Junior Engineers

What to coverDescribe a situation where you mentored a junior engineer or team member on a complex technical topic. Focus on how you identified their knowledge gaps and the specific steps you took to foster their professional growth.

Cross-functional Collaboration

What to coverProvide an example of working with diverse teams such as UI designers, researchers, and product managers to deliver an end-to-end solution. Highlight how you managed conflicting priorities or technical requirements across these groups.

Client-Facing Consulting Experience

What to coverDescribe a time you had to explain a complex AI or knowledge graph solution to a client. How did you build trust and ensure the proposed architecture aligned with their broader business systems?

Handling Technical Decision Making

What to coverTell me about a time you had to make a critical technical decision for a project that impacted multiple teams. How did you gather the necessary evidence, evaluate the trade-offs, and communicate the final decision to stakeholders?

Sharpen your knowledge

Topics to prepare

Knowledge Graph Lifecycle and Ontology Management
The role requires deep expertise in the full lifecycle of knowledge graphs, including schema design, ontology management, and curation. Prepare by reviewing how to map unstructured data to formal ontologies using RDF/SHACL and be ready to discuss how these structures enhance LLM reasoning and reduce hallucinations in enterprise applications.
LLM Architecture and Neural Network Optimization
You will be responsible for designing and refining neural network parameters for language models. Focus your revision on the trade-offs between model size, latency, and performance, as well as techniques for fine-tuning and parameter optimization to meet specific business requirements.

Skills to revise

Google Cloud Data Services
This is a mandatory skill with a 5-year experience requirement. Review the specific suite of GCP data tools, including BigQuery, Cloud Storage, and Dataflow, and practice how to integrate these services into an end-to-end AI data pipeline for LLM training and deployment.
Enterprise Data Pipeline Implementation
The role requires building scalable pipelines using tools like Apache NiFi or Airflow. Practice designing a robust ETL/ELT workflow that handles data ingestion, cleaning, and vectorization, ensuring the pipeline is optimized for feeding high-quality data into LLMs and knowledge graph components.
Graph-based Machine Learning and Vector Databases
You must demonstrate experience with graph databases (e.g., Neo4j, Stardog) and vector stores. Prepare by practicing how to combine graph-based features with vector embeddings to create hybrid search or retrieval-augmented generation (RAG) solutions that integrate with broader client systems.

Your preparation checklist

Map Google Cloud Data Services expertise
Prepare a detailed breakdown of your 5+ years of experience with Google Cloud Data Services. Create a summary document highlighting specific tools used for data ingestion, storage, and processing, and be ready to discuss how these services supported your previous AI or data engineering projects.
Develop a knowledge graph project walkthrough
Prepare a case study demonstrating your experience with RDF, SPARQL, or LPG. Focus on a project where you performed schema design, ontology management, or graph curation, and explain how the graph solution integrated with broader system architectures.
Document end-to-end LLM pipeline experience
Outline a technical project where you built an AI data pipeline. Detail the specific steps from data ingestion and curation to model deployment, highlighting your use of Python, ETL tools like Apache Airflow or NiFi, and your approach to prompt engineering for enterprise-scale applications.
Prepare for technical discussions on neural network architecture
Be ready to discuss your methodology for designing and tuning neural network parameters. Prepare to explain how you evaluate model performance and how you have balanced model complexity with scalability in previous roles.
Formulate client-facing communication examples
Prepare examples of how you have articulated the business value of complex AI or knowledge graph solutions to non-technical stakeholders. Focus on instances where you supported sales efforts or collaborated with cross-functional teams to align technical outputs with business goals.
Draft questions regarding team leadership and integration
Prepare thoughtful questions for the interviewers about the team's current approach to mentoring junior engineers, the typical cross-functional collaboration process with UI designers and researchers, and how the team balances immediate project delivery with long-term knowledge sharing.

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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