Senior Full Stack Engineer

Quick Facts

• Role: Senior Full Stack Engineer (fully remote in the U.S.)

Description

AI Engineering builds the shared platform powering AI across the product portfolio, including agent runtime, model gateway, retrieval, guardrails, evaluations, and cost controls. This role is hands-on and full-stack—turning what AI agents can do into software people rely on daily, from backend agent runtimes and APIs through to retrieval and the user interfaces for working with agents.

Responsibilities

• Build reliable agent workflows combining models, retrieval, tools, and human approval (multi-agent handoffs, long-running tasks, and persisted state)
• Improve document and business-data ingestion and retrieval; measure answer quality with automated offline and online evaluations
• Keep customer data safe through tenant isolation, permissions, prompt-injection defenses, careful handling of personal data, and audit trails
• Build reusable UI components, SDKs, and integrations (including MCP) for product teams to embed
• Design trustworthy agent interfaces with streaming responses, clear agent status, human approval steps, and accessibility built in
• Track and optimize cost, latency, and quality; trace agent runs end to end; make agents faster and cheaper
• Partner with product teams to turn early ideas into shipped features
• Lead technical design for major platform features; write clear design docs and set standards through code review
• Mentor engineers across AI Engineering and product teams; help shape how teams build with AI
• Share on-call for the services you build (supported by Site Reliability Engineers)

Requirements

• 6+ years building and shipping production software, with recent senior-level experience
• Strong Python backend experience (ideally FastAPI, Pydantic, and async services)
• Solid TypeScript and React skills, including reusable components or SDKs used by other teams
• Hands-on production experience with LLMs (tool calling, RAG, or agent workflows) and understanding of failure modes
• Experience with retrieval and search (vector databases like pgvector, hybrid search, chunking, reranking)
• Knowledge of PostgreSQL, data modeling, performance, and migrations
• Experience building multi-tenant SaaS with strong data security, permissions, and handling of personal information
• Comfort running what you build on AWS using infrastructure as code (Terraform or similar), plus CI/CD and monitoring (Datadog)
• Ability to measure and improve AI output quality, tracking cost and latency using data
• Experience leading technical design, writing design docs, and mentoring engineers
• Use AI coding tools daily with good judgment about where human review still matters
• Legally authorized to work in the U.S. (no visa sponsorship)

Benefits

• Paid Parental Leave programs
• GiveBackDays: 3 extra days off to volunteer and support social impact
• Ongoing Diversity & Inclusion initiatives, including a global mentorship program
• Access to free mental health support
• Flexible working arrangements