Sr AI Architect - Conversational AI

About the role

This position is critical to leveraging Twilio’s massive data ecosystem and unmatched communication scale to build our customer facing AI capabilities, such as Twilio Conversational Memory, Enterprise Knowledge, Behavioral Data Intelligence and many more to power the future of our customer engagement platform. As a Sr. AI Architect for Twilio Platform, you will also influence the design and evolution of our company-wide ML/AI Ops foundation.

Responsibilities

• Define and drive a long-term AI/ML architectural vision that aligns with Twilio’s business goals, specifically focusing on how data and memory power the next generation of customer engagement.
• Own the strategic roadmap for Twilio’s ML/AI Ops platform and tooling, ensuring a unified approach to model development, deployment, and lifecycle management across all platform capabilities.
• Evaluate and implement modern LLM architectures, RAG systems, MCP/tooling frameworks, and inference optimization techniques.
• Lead architecture for agentic AI systems including orchestration, reasoning, tool usage, and contextual grounding.
• Stay current with rapidly evolving advancements in LLMs, agent frameworks, reasoning systems, and AI infrastructure.
• Transition seamlessly from high-level strategic communication with executives to deep-dive code reviews and pair programming with engineers.
• Partner closely with Product Management to turn a roadmap into a sequence of technical milestones, ensuring that technical investments always map to customer value.
• Have a 'player-coach' mentality, and contribute hands-on technical expertise while providing strategic direction and mentorship to the team.

Qualifications

• 15+ years of experience in software engineering, with at least 6+ years specifically focused on building and scaling production-grade ML systems at a platform level.
• Extensive experience with ML Ops and LLM Ops patterns, including designing and implementing rigorous evaluation metrics, automated retraining loops, and monitoring for non-deterministic AI features at scale.
• Deep expertise in the design, architecture, and deployment of production-grade ML/AI systems, including deep knowledge of transformer models, LLM orchestration, embedding models, inference optimization and vector stores.
• Strong background in building cloud-based services using AWS, GCP, or Azure, with experience managing high-volume data and various data stores.
• Exceptional communication and collaboration skills, with a proven ability to mentor engineers, influence company wide technical strategy, product direction, and drive results across the company.
• A Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a closely related quantitative field.