Overview
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.
Responsibilities
- 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.
Qualifications
Required Knowledge / Skills / Abilities
- 10+ years experience in Software Engineering / ML Engineering / Data Platforms, with 3+ years in applied AI/ML and 23+ 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).
