Sr Staff Enterprise AI Architect

Job Description Summary

The Sr Staff Enterprise AI Architect will lead the design, standardization, and scaling of enterprise-grade AI solutions across strategic business initiatives. This role will define reusable AI architecture patterns, guide implementation teams on modern AI/GenAI approaches, and ensure alignment with enterprise standards for scalability, security, reliability, observability, and cost optimization.

The ideal candidate brings deep expertise in Generative AI solution architecture, Retrieval-Augmented Generation (RAG), agentic AI systems, LLM orchestration, enterprise data integration, and AI platform engineering. This individual will work closely with business, product, data, and engineering teams to accelerate the adoption of reusable AI capabilities and production-ready AI solutions across the enterprise.

• Define and drive enterprise AI architecture strategy across multiple business domains and AI initiatives.
• Architect scalable, reusable, and secure AI/GenAI solution patterns for enterprise adoption.
• Lead solution design for Generative AI/ Physical AI/ Vision AI applications including RAG pipelines, agentic workflows, AI copilots, document intelligence, and enterprise search.
• Establish enterprise standards for:
  • Model selection and orchestration
  • Prompt engineering and prompt management
  • Chunking and embedding strategies
  • Hybrid retrieval approaches
  • Vector database integration
  • Agentic orchestration frameworks
  • AI observability, evaluation, and monitoring
• Guide teams on advanced retrieval architectures, semantic search, non-sequential document understanding, and contextual information extraction.
• Design reusable AI services and accelerators that can be leveraged across multiple use cases and business units.
• Collaborate with enterprise platform, cloud, data, and cybersecurity teams to ensure AI solutions align with enterprise architecture and governance standards.
• Standardize integration patterns with enterprise systems including data lakes, ERP systems, workflow platforms, and operational applications.
• Drive architectural decisions balancing scalability, maintainability, performance, security, and cost efficiency.
• Define enterprise AI evaluation frameworks covering accuracy, hallucination tracking, latency, reliability, and operational KPIs.
• Influence AI platform roadmap and foundational AI services by providing architectural guidance and identifying capability gaps.
• Conduct architecture reviews and provide technical leadership for critical AI programs and production deployments.
• Partner with engineering and product teams to accelerate AI solution delivery through reusable frameworks and best practices.
• Mentor and upskill engineering teams on enterprise AI architecture patterns, GenAI best practices, and emerging AI technologies.
• Stay current with evolving AI ecosystem trends, frameworks, foundation models, and enterprise AI architectural practices.

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related technical discipline from an accredited institution.
• Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field is preferred.

• 12+ years of experience in enterprise software architecture, platform engineering, or digital technology leadership roles.
• 5+ years of experience in AI/ML solution architecture with strong focus on enterprise AI and Generative AI systems.
• Strong expertise in:
  • Generative AI and LLM-based solution architecture
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI frameworks and orchestration
  • Prompt engineering and evaluation techniques
  • Vector databases and semantic retrieval systems
  • AI observability and evaluation frameworks
• Experience designing enterprise-scale AI platforms on major cloud providers such as Amazon Web Services / Microsoft Azure
• Strong understanding of enterprise data architecture, APIs, integration patterns, and distributed systems.
• Experience integrating AI solutions with enterprise platforms such as Databricks, ERP systems, document repositories, and operational systems.
• Familiarity with modern AI/ML frameworks and orchestration technologies such as LangChain, LangGraph, Semantic Kernel, or equivalent frameworks.
• Strong understanding of AI governance, security, responsible AI, and enterprise compliance considerations.
• Demonstrated ability to influence technical strategy and drive alignment across cross-functional engineering and business teams.
• Excellent communication, stakeholder management, and executive presentation skills.
• Ability to operate effectively in fast-paced, highly collaborative, and ambiguous environments.
• Strong problem-solving mindset with the ability to balance innovation with pragmatic enterprise delivery considerations.