Large Language Model Architect

About Accenture

Qualifications

• Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
• Minimum 15+ years of overall experience across software engineering, data engineering, AI/ML engineering, cloud architecture, enterprise architecture or technology leadership.
• Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics or cloud-native solutions using at least one cloud vendor.
• Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic systems, RAG, prompt engineering, model integration and evaluation patterns.
• Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in enterprise-scale or production environments.
• Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering or large-scale analytical engineering applications.
• Minimum 6+ years of experience as a machine learning / data / AI architect designing large-scale analytical engineering solutions in industry contexts such as financial services, healthcare, retail, manufacturing, telecom or media.
• Required Skills/ Experience
• Deep architecture and hands-on engineering experience with Gemini Enterprise Agent Platform / Vertex AI, Gemini models, Model Garden, Vertex AI Search, BigQuery, AlloyDB/Cloud SQL vector search, GKE, Cloud Run, Cloud Functions, Pub/Sub, IAM, Cloud Logging, Cloud Monitoring and VPC Service Controls.
• Strong expertise in enterprise AI platform architecture covering RAG, embeddings, vector databases, semantic retrieval, context engineering, model routing, agent orchestration, memory, tool calling, AI gateways and model evaluation.
• Ability to set enterprise NFRs and architectural controls for performance, scalability, security, privacy, reliability, governance, observability, resiliency, cost optimization and operational readiness.
• Experience making definitive, evidence-based decisions on design patterns, reference architectures, frameworks, technology selections, foundation models and deployment approaches.
• Experience establishing agent registry and certification models, AI control planes, access models, guardrails, production evaluation stacks, model risk controls and cross-platform governance.
• Strong executive communication, architecture governance and thought leadership skills with ability to influence business, technology, security, product and delivery leadership teams.
• Good to Have Skills
• Google Cloud Professional Cloud Architect, Professional ML Engineer or Professional Data Engineer certification experience with BigQuery semantic layers, Dataform/dbt, Vertex AI pipelines, GKE, Looker, LangChain/LlamaIndex, VPC Service Controls and regulated data architecture.
• Exposure to open-source AI and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker and Kubernetes.
• Experience with responsible AI, model risk management, AI governance boards, red-teaming, synthetic data, human-in-the-loop review, A/B testing and GenAI FinOps.
• Recognized thought leadership through enterprise reference architectures, internal capability building, client advisory, platform accelerators, publications, whitepapers, conference sessions or industry forums.
• 15 years full time education

Responsibilities

• Partner with client executives and business leaders to define the enterprise AI strategy, target-state architecture and investment roadmap across platforms, data, models, applications and operating model.
• Lead enterprise AI assessments, technology comparisons, platform selection, reference architecture definition, modernization opportunities and implementation sequencing for complex transformations.
• For GCP, set the GCP enterprise AI platform strategy define Gemini and Vertex AI reference architecture for enterprise agents, grounding, tool orchestration, governed RAG and high-scale inference guide use of BigQuery, Vertex AI Search, vector stores and GKE/Cloud Run for platformized AI delivery establish governance, security perimeter, observability and evaluation standards.
• Own the complete end-to-end technical solution for complex AI platforms, ensuring every domain is designed cohesively against business objectives, enterprise standards and non-functional requirements.
• Translate governing architecture principles into a concrete, defensible technical solution that platform, data, AI/ML and application engineering teams can build against.
• Set architectural direction for model- and tool-agnostic multi-agent systems, including orchestration, memory, tool/skill use, agent registry, AI gateway/control plane, risk scoring and certification gates.
• Define the enterprise context layer architecture across knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly, conversational state and reusable memory services.
• Establish identity, authorization, layered guardrails, prompt-injection defense, PII protection, audit logging, lineage and defense-in-depth controls for AI agents, tools, data and models.
• Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, model quality, latency, cost, safety, reliability, production support and continuous improvement.
• Establish FinOps as a first-class AI concern, including usage labelling, token budgets, gateway-enforced budgets, cost-per-archetype planning, threshold alerts and optimization levers.
• Produce and steward authoritative architecture assets including enterprise AI blueprints, ADRs, sequence diagrams, solution patterns, interface specifications, reference architectures and governance playbooks.