Sr AI Architect

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

Required:

• 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.
• Deep understanding of the Context Engineering lifecycle, including semantic retrieval, contextual compression, state management across multi-turn conversations.
• 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.

Desired:

• A track record of relevant publications at top ML conferences or significant open-source contributions.
• Experience designing evaluation frameworks that specifically measure context quality.
• Track record of designing and implementing enterprise-scale ML/AI Ops platforms.
• Experience working in a geographically distributed environment.