AI Infrastructure 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 10+ years of experience in software engineering, data engineering, AI/ML engineering or technology architecture.
• Minimum 5+ years of experience designing and deploying enterprise-grade advanced AI or cloud data solutions using at least one cloud vendor.
• Minimum 2+ years of experience in agentic AI, LLM and generative AI solution architecture or engineering delivery.
• Minimum 4+ years of coding experience using Python and experience with APIs, distributed systems, reusable frameworks and cloud-native application patterns.
• Minimum 4+ years of experience in ML, deep learning, NLP, data engineering, analytical engineering or AI product delivery.
• Demonstrated experience as a solution/technology architect in industry contexts such as banking, insurance, healthcare, public sector, retail or energy.
• Required Skills/ Experience
• Hands-on architecture and engineering experience with Microsoft Foundry / Azure AI Foundry, Foundry Agent Service, Azure OpenAI models, Azure AI Search, Prompt Flow, Azure Functions, Logic Apps, API Management, Event Grid, Microsoft Fabric, Entra ID, Key Vault, Azure Monitor and Application Insights.
• Strong knowledge of LLM architecture patterns including RAG, embeddings, vector databases, prompt engineering, model routing, fine-tuning/adaptation, function calling, tool integration and agent orchestration.
• Ability to define enterprise AI platform patterns for performance, scalability, security, reliability, observability, governance, cost optimization and operational support.
• Experience designing reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses and deployment pipelines.
• Experience with CI/CD, infrastructure-as-code, automated testing, model evaluation, MLOps/LLMOps, monitoring and production release governance.
• Strong stakeholder management skills with ability to communicate architecture trade-offs, risks and recommendations to engineering, product, security and leadership teams.
• Good to Have Skills
• Azure Solutions Architect Expert, Azure AI Engineer Associate or Azure Developer certification experience with Microsoft Fabric, Semantic Kernel, Prompt Flow, private endpoints, Defender for Cloud, Microsoft 365 Copilot integration and enterprise Responsible AI controls.
• 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, human-in-the-loop review, A/B testing and GenAI FinOps.
• Experience building reusable enterprise reference architectures, estimation models, accelerators, playbooks and architecture governance frameworks.
• 15 years full time education

Responsibilities

• Translate business strategy and product goals into a technical vision, architecture blueprint, non-functional requirements and implementation roadmap.
• Lead stakeholder workshops to align on feasibility, project scope, solution boundaries, delivery dependencies and client-facing expectations.
• For Azure, define Azure AI/LLM architecture and reference patterns design agent systems with Foundry Agent Service, Azure OpenAI and Azure AI Search establish enterprise integration through API Management, Logic Apps and Functions govern identity, private networking, observability, policy controls and responsible AI gates.
• Architect model and tool agnostic multi-agent systems, including orchestration, tool use, agent memory, context management, MCP/control-plane patterns and reusable service abstractions.
• Design the end-to-end data and context layer including ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs and semantic retrieval for reliable RAG.
• Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security and operational reliability.
• Establish AI security, governance and observability as centrally enforced design controls including guardrails, prompt-injection defense, PII protection, access control, audit logging and OpenTelemetry-style tracing.
• Maintain architecture decision records, component diagrams, sequence diagrams, design specifications, integration patterns and reusable reference architecture assets.