# Sr AI Architect - Conversational AI

**Company**: Twilio
**Location**: Remote - US
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $275,840.00 - 344,800.00
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/twilio/jobs/7926891?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_56101d31-e06

## Description

At Twilio, we're shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences.

As a Sr. AI Architect for Twilio Platform, you will leverage Twilio's massive data ecosystem and unmatched communication scale to build customer-facing AI capabilities, such as Twilio Conversational Memory, Enterprise Knowledge, and Behavioral Data Intelligence.

In this role, you'll:

- Define and drive a long-term AI/ML architectural vision that aligns with Twilio's business goals, 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.

Required 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.

- 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.

Location: Remote - US

This role will involve approximately 5% travel.

We offer competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more.

## Skills

### Required
- ML Ops
- LLM Ops
- Transformer models
- LLM orchestration
- Embedding models
- Inference optimization
- Vector stores
- Context Engineering
- Semantic retrieval
- Contextual compression
- State management
- Cloud-based services
- AWS
- GCP
- Azure

### Nice to have
- Publications at top ML conferences
- Open-source contributions
- Experience designing evaluation frameworks
- Track record of designing and implementing enterprise-scale ML/AI Ops platforms
- Experience working in a geographically distributed environment

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/twilio/jobs/7926891?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)