Large Language Model Architect
Accenture · Bengaluru · onsite · Posted 56d ago
Not disclosed7–9 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
Project Role : Large Language Model Architect
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Google Cloud Data Services
Good to have skills : Large Language Models (LLMs)
Minimum 7.5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing, building, integrating, testing and operationalizing enterprise-grade LLM, GenAI and agentic AI components across active client engagements.
Own platform-specific engineering on GCP, translating high-level architecture into working, production-quality components for LLM-driven applications, RAG pipelines, multi-agent workflows and AI platform integrations.
Bring practical industry experience in financial services, healthcare, retail, manufacturing, telecom or media to identify domain data, process constraints, controls and adoption risks while designing GenAI solutions that are safe, scalable and relevant.
Operate as a hands-on technical lead or engineering lead, contributing code, design decisions, reusable patterns and engineering documentation.
Key Responsibilities
Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.
Build Gemini-powered LLM applications and agents implement grounding/RAG with BigQuery, Vertex AI Search and vector stores orchestrate tool calling and event-driven workflows evaluate model output quality and deploy AI services on Cloud Run or GKE.
Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.
Engineer safety and control components including PII detection/redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage and audit logging.
Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.
Maintain technical artifacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.
Required Qualifications
Bachelor s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.
Experience delivering AI/ML or data products in at least one industry domain such as financial services, healthcare, retail, manufacturing, telecom or media.
Required Skills/ Experience
Hands-on experience with Vertex AI / Gemini Enterprise Agent Platform, Gemini models, Model Garden, Agent Builder/Agent Engine patterns, Vertex AI Search, BigQuery, AlloyDB/Cloud SQL vector support, Cloud Run, GKE, Pub/Sub, Cloud Functions, IAM, Cloud Logging and Monitoring.
Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.
Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.
Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.
Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.
Good to Have Skills
Google Cloud Professional Data Engineer, Professional ML Engineer or Cloud Architect certification experience with BigQuery-based semantic layers, Dataform/dbt, LangChain/LlamaIndex, GKE ML workloads and MLOps on Vertex AI.
Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.
Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimization.
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Google Cloud Data Services
Good to have skills : Large Language Models (LLMs)
Minimum 7.5 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing, building, integrating, testing and operationalizing enterprise-grade LLM, GenAI and agentic AI components across active client engagements.
Own platform-specific engineering on GCP, translating high-level architecture into working, production-quality components for LLM-driven applications, RAG pipelines, multi-agent workflows and AI platform integrations.
Bring practical industry experience in financial services, healthcare, retail, manufacturing, telecom or media to identify domain data, process constraints, controls and adoption risks while designing GenAI solutions that are safe, scalable and relevant.
Operate as a hands-on technical lead or engineering lead, contributing code, design decisions, reusable patterns and engineering documentation.
Key Responsibilities
Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.
Build Gemini-powered LLM applications and agents implement grounding/RAG with BigQuery, Vertex AI Search and vector stores orchestrate tool calling and event-driven workflows evaluate model output quality and deploy AI services on Cloud Run or GKE.
Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.
Engineer safety and control components including PII detection/redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage and audit logging.
Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.
Maintain technical artifacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.
Required Qualifications
Bachelor s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.
Experience delivering AI/ML or data products in at least one industry domain such as financial services, healthcare, retail, manufacturing, telecom or media.
Required Skills/ Experience
Hands-on experience with Vertex AI / Gemini Enterprise Agent Platform, Gemini models, Model Garden, Agent Builder/Agent Engine patterns, Vertex AI Search, BigQuery, AlloyDB/Cloud SQL vector support, Cloud Run, GKE, Pub/Sub, Cloud Functions, IAM, Cloud Logging and Monitoring.
Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.
Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.
Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.
Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.
Good to Have Skills
Google Cloud Professional Data Engineer, Professional ML Engineer or Cloud Architect certification experience with BigQuery-based semantic layers, Dataform/dbt, LangChain/LlamaIndex, GKE ML workloads and MLOps on Vertex AI.
Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.
Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimization.
Sourced from foundit
Skills
Testingmodel invocationRAG functionobservabilityPrompt EngineeringDistributed Systemsaws iamapisautomated testinginfrastructure as codegeminibigqueryAgile Deliveryvector databasesEmbeddingsAgent orchestrationGoogle Cloud RunGoogle Vertex AIGoogle Kubernetes Engineevaluation metricstool callingCloud Data ServicesGoogle Cloud FunctionsGoogle Cloud Pub Subsecure SDLC practicesPythonGit-based developmentCi/CdCloud logging and monitoring
About Accenture
Accenture
IT Services & Consulting
350k+
employees
1989
37 years old
Dublin
Ireland
About
Accenture plc is an Irish-American, professional services company based in Dublin, specializing in information technology services and consulting.
Industries
IT Services & ConsultingAnalytics & KPOSoftware ProductManagement Consulting
Employee ratings
73,428 reviews
3.7/ 5
Culture
3.7
Career growth
2.8
Work-life
3.6
Employees rate it well for
Work Life Balance8779Work Culture7586Salary7496Learning5602Senior Management4852
Find them on