Sr AI Architect - Conversational AI

Twilio Twilio · Enterprise · United States · Remote · Engineering

Sr. AI Architect focused on Conversational AI, responsible for defining the architectural vision, ML/AI Ops foundation, and driving the development of customer-facing AI capabilities like conversational memory and enterprise knowledge. The role involves evaluating and implementing LLM architectures, RAG systems, agentic AI systems, and inference optimization, while ensuring adherence to responsible AI principles and transitioning research to production.

What you'd actually do

  1. 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.
  2. 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.
  3. Evaluate and implement modern LLM architectures, RAG systems, MCP/tooling frameworks, and inference optimization techniques.
  4. Lead architecture for agentic AI systems including orchestration, reasoning, tool usage, and contextual grounding.
  5. Stay current with rapidly evolving advancements in LLMs, agent frameworks, reasoning systems, and AI infrastructure.

Skills

Required

  • software engineering
  • building and scaling production-grade ML systems
  • ML Ops
  • LLM Ops
  • evaluation metrics
  • automated retraining loops
  • monitoring for non-deterministic AI features
  • transformer models
  • LLM orchestration
  • embedding models
  • inference optimization
  • vector stores
  • Context Engineering lifecycle
  • semantic retrieval
  • contextual compression
  • state management across multi-turn conversations
  • cloud-based services (AWS, GCP, Azure)
  • high-volume data management
  • data stores
  • communication skills
  • collaboration skills
  • mentorship
  • technical strategy
  • product direction
  • Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a closely related quantitative field

Nice to have

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

What the JD emphasized

  • building and scaling production-grade ML systems at a platform level
  • 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
  • Deep understanding of the Context Engineering lifecycle
  • building cloud-based services using AWS, GCP, or Azure
  • Exceptional communication and collaboration skills
  • A Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a closely related quantitative field

Other signals

  • architectural vision
  • ML/AI Ops foundation
  • responsible AI principles
  • LLM architectures
  • RAG systems
  • agentic AI systems