- Drive the end-to-end architecture and technical visionfor 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 productionwith realistic, production-grade designs – covering orchestration, retrieval (RAG), evaluation, observability, guardrails, and human-in-the-loop.
- Integrate GenAIinto the existing cloud solutions and automation platform (AWS, CI/CD toolchain, ITSM) so GenAI is a first-class, governed capability.
- Design for scale and operational excellencefor GenAI workloads – throughput, latency, reliability, and cost optimization (token economics, caching, model routing).
- Establish the operational foundationincluding evaluation pipelines, monitoring, drift/quality management, and incident response for Agentic solutions.
- Bake in guardrailssuch as security, data privacy, responsible-AI, hallucination mitigation, and regulatory compliance into every architecture.
- Advise and influenceto CXO-level leaders, translating complex AI concepts into clear business value, trade-offs, risks, and roadmaps.
- Shape the generative-AI strategy and roadmapfor the enterprise, aligning technology investment with business outcomes and priorities.
- Build and presentbusiness 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.
- Mentor and upskillengineering teams and set architecture governance, review gates, and reusable building blocks.
- Define and stewardAI governance, standards, and best practices in partnership with security, data, and legal.
Requirements
- Bachelor's or Master's degreein Computer Science, Engineering, or a related field – or equivalent practical experience.
- 10+ years of experiencein software/AI engineering and architecture, including senior technical leadership on large-scale systems.
- Recognized depthin generative AI: LLMs, prompt/context engineering, RAG, agent frameworks (e.g., LangChain, Amazon Bedrock Agents), and agent harnesses.
- Proven track recorddesigning and running GenAI solutions in production at scale – including evaluation, observability, cost, and reliability.
- Deep hands-on knowledgeof AWS and its AI services (e.g., Amazon Bedrock), plus core cloud infrastructure (compute, networking, IAM, containers).
- Strong groundingwith enterprise architecture practices, integration, and the modern DevOps toolchain (GitHub, Jenkins, Artifactory, SonarQube).
- Exceptionalcommunication and executive-presence skills – able to hold credible, persuasive CXO-level conversations and articulate complex technology in business terms.
- Solid understandingof responsible-AI, security, and data-governance considerations for enterprise AI.
Core Competencies
Demonstrates expertise in generative AI architecture and implementation, with a strong focus on AWS cloud solutions, operational excellence, and AI governance. Capable of translating complex AI concepts into business value while mentoring engineering teams and influencing executive leadership.
Highest-signal resume keywords
- Generative AI Architecture
- AWS AI Services
- Large-Scale Systems Design
- AI Governance and Compliance
- Executive Communication
ATS Optimization Keywords
Hard Skills
- Generative AI
- LLMs
- Prompt Engineering
- RAG
- Agent Frameworks
- Production-Grade Design
- Cost Optimization
- Evaluation Pipelines
- Monitoring and Incident Response
- Data Privacy
Soft Skills
- Exceptional Communication
- Executive Presence
- Mentoring
Certifications & Qualifications
- Bachelor's Degree in Computer Science
- Master's Degree in Engineering
Industry Keywords
- AI Engineering
- Enterprise Architecture
- DevOps
- Security
- Data Governance
Tools & Technologies
- AWS
- GitHub
- Jenkins
- Artifactory
- SonarQube