AI Architect – Generative AI & Enterprise Solutions

Job Overview:

Responsibilities

• Architecture & Technical Leadership
• Drive the end-to-end architecture and technical vision for generative AI within the function – reference architectures, patterns, and standards that teams build against.
• Make authoritative technology decisions, selecting the right models, frameworks, and agent harnesses for each use case, balancing capability, latency, cost, and risk.
• Move solutions from proof-of-concept to production with realistic, production-grade designs – covering orchestration, retrieval (RAG), evaluation, observability, guardrails, and human-in-the-loop.
• Integrate GenAI into the existing cloud solutions and automation platform (AWS, CI/CD toolchain, ITSM) so GenAI is a first-class, governed capability.
• Running AI at Scale
• Design for scale and operational excellence for GenAI workloads – throughput, latency, reliability, and cost optimization (token economics, caching, model routing).
• Establish the operational foundation including evaluation pipelines, monitoring, drift/quality management, and incident response for Agentic solutions.
• Bake in guardrails such as security, data privacy, responsible-AI, hallucination mitigation, and regulatory compliance into every architecture.
• Executive Engagement & Strategy
• Advise and influence to CXO-level leaders, translating complex AI concepts into clear business value, trade-offs, risks, and roadmaps.
• Shape the generative-AI strategy and roadmap for the enterprise, aligning technology investment with business outcomes and priorities.
• Build and present business cases, ROI, and build-vs-buy analyses for AI initiatives.
• Serve as an evangelist and trusted expert – to executives, engineering teams, and external partners – and champion an enterprise AI vision.
• Enablement & Governance
• Mentor and upskill engineering teams and set architecture governance, review gates, and reusable building blocks.
• Define and steward AI governance, standards, and best practices in partnership with security, data, and legal.
• Architecture & Technical Leadership - Drive the end-to-end architecture and technical vision for generative AI within the function – reference architectures, patterns, and standards that teams build against. - Make authoritative technology decisions, selecting the right models, frameworks, and agent harnesses for each use case, balancing capability, latency, cost, and risk. - Move solutions from proof-of-concept to production with realistic, production-grade designs – covering orchestration, retrieval (RAG), evaluation, observability, guardrails, and human-in-the-loop. - Integrate GenAI into the existing cloud solutions and automation platform (AWS, CI/CD toolchain, ITSM) so GenAI is a first-class, governed capability. Running AI at Scale - Design for scale and operational excellence for GenAI workloads – throughput, latency, reliability, and cost optimization (token economics, caching, model routing). - Establish the operational foundation including evaluation pipelines, monitoring, drift/quality management, and incident response for Agentic solutions. - Bake in guardrails such as security, data privacy, responsible-AI, hallucination mitigation, and regulatory compliance into every architecture. Executive Engagement & Strategy - Advise and influence to CXO-level leaders, translating complex AI concepts into clear business value, trade-offs, risks, and roadmaps. - Shape the generative-AI strategy and roadmap for the enterprise, aligning technology investment with business outcomes and priorities. - Build and present business cases, ROI, and build-vs-buy analyses for AI initiatives. - Serve as an evangelist and trusted expert – to executives, engineering teams, and external partners – and champion an enterprise AI vision. Enablement & Governance - Mentor and upskill engineering teams and set architecture governance, review gates, and reusable building blocks. - Define and steward AI governance, standards, and best practices in partnership with security, data, and legal.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field – or equivalent practical experience.
• 10+ years of experience in software/AI engineering and architecture, including senior technical leadership on large-scale systems.
• Recognized depth in generative AI: LLMs, prompt/context engineering, RAG, agent frameworks (e.g., LangChain, Amazon Bedrock Agents), and agent harnesses.
• Proven track record designing and running GenAI solutions in production at scale – including evaluation, observability, cost, and reliability.
• Deep hands-on knowledge of AWS and its AI services (e.g., Amazon Bedrock), plus core cloud infrastructure (compute, networking, IAM, containers).
• Strong grounding with enterprise architecture practices, integration, and the modern DevOps toolchain (GitHub, Jenkins, Artifactory, SonarQube).
• Exceptional communication and executive-presence skills – able to hold credible, persuasive CXO-level conversations and articulate complex technology in business terms.
• Solid understanding of responsible-AI, security, and data-governance considerations for enterprise AI.
• - Bachelor's or Master's degree in Computer Science, Engineering, or a related field – or equivalent practical experience. - 10+ years of experience in software/AI engineering and architecture, including senior technical leadership on large-scale systems. - Recognized depth in generative AI: LLMs, prompt/context engineering, RAG, agent frameworks (e.g., LangChain, Amazon Bedrock Agents), and agent harnesses. - Proven track record designing and running GenAI solutions in production at scale – including evaluation, observability, cost, and reliability. - Deep hands-on knowledge of AWS and its AI services (e.g., Amazon Bedrock), plus core cloud infrastructure (compute, networking, IAM, containers). - Strong grounding with enterprise architecture practices, integration, and the modern DevOps toolchain (GitHub, Jenkins, Artifactory, SonarQube). - Exceptional communication and executive-presence skills – able to hold credible, persuasive CXO-level conversations and articulate complex technology in business terms. - Solid understanding of responsible-AI, security, and data-governance considerations for enterprise AI.