AI Infrastructure Architect
Accenture · Bengaluru · onsite · Posted 50d ago
Undisclosed5–10 yrsfulltimeSourced from
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About this role
Project Role : AI Infrastructure Architect
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Design and guide implementation of production-grade agentic AI solutions, with hands-on ownership of critical architectural components and integration patterns.
Hands-on proficiency building reference implementations with LangGraph, OpenAI Agents SDK, Google ADK, PydanticAI or CrewAI, and using Cursor, Claude Code or Codex to accelerate coding while retaining control over orchestration, state, testing and failure recovery.
Must have personally designed and implemented major components of AI systems that reached production. Prototype-only experience does not meet the requirement.
Roles Responsibilities:
- Design agent workflows, orchestration, tool use, memory, retrieval, and human-in-the-loop patterns.
- Translate business requirements into scalable technical designs.
- Build reference implementations and validate architecture through working code.
- Integrate agents with enterprise APIs, databases, workflow systems, and identity platforms.
- Define evaluation, tracing, guardrails, recovery, and operational monitoring.
- Review code and designs, resolve technical risks, and mentor engineers.
- Support production readiness, performance tuning, and incident resolution.
Professional Technical Skills:
- Hands-on Python and modern agentic or GenAI frameworks.
- RAG, vector and graph retrieval, prompt and context engineering, and structured outputs.
- Microservices, APIs, queues, containers, cloud deployment, and CI/CD.
- Security, observability, model evaluation, latency, and cost controls.
Qualification
15 years full time education
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
Design and guide implementation of production-grade agentic AI solutions, with hands-on ownership of critical architectural components and integration patterns.
Hands-on proficiency building reference implementations with LangGraph, OpenAI Agents SDK, Google ADK, PydanticAI or CrewAI, and using Cursor, Claude Code or Codex to accelerate coding while retaining control over orchestration, state, testing and failure recovery.
Must have personally designed and implemented major components of AI systems that reached production. Prototype-only experience does not meet the requirement.
Roles Responsibilities:
- Design agent workflows, orchestration, tool use, memory, retrieval, and human-in-the-loop patterns.
- Translate business requirements into scalable technical designs.
- Build reference implementations and validate architecture through working code.
- Integrate agents with enterprise APIs, databases, workflow systems, and identity platforms.
- Define evaluation, tracing, guardrails, recovery, and operational monitoring.
- Review code and designs, resolve technical risks, and mentor engineers.
- Support production readiness, performance tuning, and incident resolution.
Professional Technical Skills:
- Hands-on Python and modern agentic or GenAI frameworks.
- RAG, vector and graph retrieval, prompt and context engineering, and structured outputs.
- Microservices, APIs, queues, containers, cloud deployment, and CI/CD.
- Security, observability, model evaluation, latency, and cost controls.
Qualification
15 years full time education
Sourced from naukri
Skills
AI infrastructureCloud DeploymentclaudeCrewaiapisBusiness RequirementsClaude CodecursorIncident ResolutionLarge Language Models (LLMs)deploymentcloudArchitectureFull StackMicroservicesAi SolutionsArtificial Intelligence
About Accenture
Accenture
IT Services & Consulting
350k+
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1989
37 years old
Dublin
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