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Accenture

Advises businesses and runs their technology

AI Infrastructure Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, Artificial Intelligence/Machine Learning, Information Technology, or a related engineering discipline.
  • At least 10 years of experience in software engineering, data engineering, artificial intelligence/machine learning engineering, or technology architecture.
  • At least 5 years of experience designing and deploying enterprise-grade advanced artificial intelligence or cloud data solutions using at least one cloud vendor.
  • At least 2 years of experience in agentic artificial intelligence, large language model, and generative artificial intelligence solution architecture or engineering delivery.
  • At least 4 years of coding experience using Python, and experience with application programming interfaces, distributed systems, reusable frameworks, and cloud-native application patterns.
  • At least 4 years of experience in machine learning, deep learning, natural language processing, data engineering, analytical engineering, or artificial intelligence product delivery.
  • Demonstrated experience as a solution or technology architect in banking, insurance, healthcare, public sector, retail, or energy.
  • 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 large language model architecture patterns, including retrieval-augmented generation, embeddings, vector databases, prompt engineering, model routing, fine-tuning or adaptation, function calling, tool integration, and agent orchestration.
  • Ability to define enterprise artificial intelligence platform patterns for performance, scalability, security, reliability, observability, governance, cost optimization, and operational support.
  • Experience designing reusable agent services, memory services, application programming interface gateways, integration adapters, orchestration layers, evaluation harnesses, and deployment pipelines.
  • Experience with continuous integration and continuous delivery, infrastructure as code, automated testing, model evaluation, machine learning operations/large language model operations, monitoring, and production release governance.
  • Strong stakeholder management skills, including communicating architecture trade-offs, risks, and recommendations to engineering, product, security, and leadership teams.

Responsibilities

  • Architect and build custom artificial intelligence infrastructure and hardware solutions.
  • Optimize artificial intelligence infrastructure and hardware performance, power consumption, cost, and computational-stack scalability.
  • Advise on artificial intelligence infrastructure technology and vendor evaluation, selection, and full-stack integration.
  • Design and deliver end-to-end artificial intelligence platform architectures on Azure.
  • Own technical architecture for modern artificial intelligence systems spanning classical machine learning, generative artificial intelligence, large language model applications, retrieval-augmented generation, agentic workflows, and enterprise artificial intelligence platform integration.
  • Serve as technical authority in one or more architecture domains, such as agentic application design, artificial intelligence security and trust, artificial intelligence operations and observability, data and knowledge engineering, model platforms, and inference.
  • Apply practical industry experience to shape domain-grounded solutions, define controls, and align artificial intelligence architecture with business processes and enterprise standards.
  • 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.
  • Define Azure artificial intelligence and large language model 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; and govern identity, private networking, observability, policy controls, and responsible artificial intelligence gates.
  • Architect model- and tool-agnostic multi-agent systems, including orchestration, tool use, agent memory, context management, Model Context Protocol/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 retrieval-augmented generation.
  • Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security, and operational reliability.
  • Establish artificial intelligence security, governance, and observability as centrally enforced design controls, including guardrails, prompt-injection defense, personally identifiable information 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.

Desired Qualifications

  • 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 artificial intelligence controls.
  • Exposure to open-source artificial intelligence and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, and Kubernetes.
  • Experience with responsible artificial intelligence, model risk management, artificial intelligence governance boards, red-teaming, human-in-the-loop review, A/B testing, and generative artificial intelligence FinOps.
  • Experience building reusable enterprise reference architectures, estimation models, accelerators, playbooks, and architecture governance frameworks.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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