The Sr. AI Architect will lead the internal AI team to design, develop, and deploy cutting-edge AI solutions that drive business transformation and operational efficiency. This role requires strategic vision, technical expertise, and leadership skills to align AI initiatives with organizational goals and ensure ethical and responsible AI practices. 

• Define overarching AI architecture including patterns for RAG, multi-agent systems, and workflow orchestration. 
• Create and own AI reference architectures, blueprints, and design patterns for product teams.
• Evaluate and recommend vendors and technologies (OpenAI, Anthropic, Google, Meta, etc.).
• Architect robust RAG and knowledge systems with taxonomies, metadata standards, and semantic caching.
• Define architectural guardrails for data privacy, encryption, and secure connectivity.
• Establish Responsible AI frameworks focusing on fairness, bias mitigation, and explainability.
• Architect end-to-end observability for AI systems and integrate workflows into CI/CD pipelines.
• Provide architectural leadership and partner with product leaders to translate strategy into actionable roadmaps. 

Required Knowledge / Skills / Abilities 

• 10+ years experience in Software Engineering / ML Engineering / Data Platforms, with 3+ years in applied AI/ML and 2–3+ years in an architect/principal role. 
• Deep experience designing and operating cloud-native systems on AWS, GCP, or Azure. 
• Strong expertise in Python and TypeScript/JavaScript ecosystems. 
• Proven track record architecting and delivering production AI/LLM systems including RAG architectures and multi-step workflows. 
• Strong understanding of data architecture, security, and compliance considerations for AI. 
• Excellent communication and leadership skills for engaging with executives and engineering teams. 

Nice-to-Have Skills / Abilities 

• Experience with multimodal and real-time agents (voice, vision, document understanding, OCR). 
• Background in building internal platforms or Centers of Excellence for AI. 
• Familiarity with AI product management concepts and emerging AI stack components. 
• Experience in regulated or data-sensitive environments (finance, healthcare, housing).