[Hiring] Senior AI Platform Engineer @Infinity

Role Description

As a Senior AI Platform Engineer, you'll be on the frontlines of our most critical customer implementations, with a strong focus on conversational AI agents deployed in real business environments. You'll design, build, and deliver agentic systems that handle live users, multi-turn conversations, real-time constraints, and complex integrations. These are not demos or experiments — they are production systems that customers rely on.

Beyond hands-on engineering, you will act as a technical owner for client delivery. You'll translate customer requirements and SOWs into working systems, own delivery timelines, manage technical tradeoffs, and ensure successful outcomes in production. This is a hands-on role. You're not just reviewing PRs or sitting in meetings — you're in the weeds, building systems, debugging failures, and showing others how it's done.

Responsibilities

• Build advanced AI agent workflows on n8n and Supernal's proprietary platform
• Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage
• Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch
• Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables
• Make and own architectural decisions across LLM orchestration, RAG design, API integrations, and workflow decomposition
• Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery
• Write automated tests — unit tests for isolated logic and end-to-end tests for full workflow and user journey validation
• Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures
• Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies
• Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation
• Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence
• Define, document, and evolve best practices for building and delivering reliable AI Employees

Qualifications

• 4+ years of experience as a software engineer, automation engineer, or systems builder shipping production systems
• Hands-on experience deploying voice agents using leading platforms (e.g., ElevenLabs, Retell, Nextiva), including telephony and streaming audio integration patterns
• Understanding of multi-turn conversation design: state management, context windows, interruption handling, and graceful recovery
• Experience tackling real-time constraints in production: latency budgets, streaming audio, fallback paths, and API chaos
• Ability to write automated tests as a matter of course — unit tests, integration tests, and end-to-end workflow validation
• Solid engineering fundamentals: error handling, retry strategies, separation of concerns, and clean interfaces between components
• Comfortable owning delivery outcomes end-to-end — not just writing code — including timelines, reliability, and client success
• Deep experience with agentic architectures, workflow automation platforms (n8n, Zapier, Make), and APIs
• Understanding of LLM orchestration, prompt engineering, function calling, and retrieval-augmented generation (RAG)
• Ability to prototype fast and finish the job to production quality — with tests, error handling, monitoring, and runbooks
• Elite debugging skills to reason through edge cases, flaky agents, and real-world API failures
• Clear and fluent communication in English — both in writing and verbally
• Ability to run meetings, drive decisions, write crisp updates, and ask the right questions early
• Thrive in fast-paced, ambiguous startup environments and take ownership without being asked
• Bring a low-ego, high-integrity approach to collaboration and leadership

What Success Looks Like

• Voice-first AI Employees are delivered on time, meet customer requirements, and perform reliably in production
• Client implementations are predictable, well-architected, and resilient under real-world conditions
• Complex conversational and voice workflows behave consistently and recover gracefully from failure
• Code is well-tested, well-documented, and maintainable — not just functional
• Technical decisions are communicated clearly and proactively to stakeholders, with tradeoffs explained and risks surfaced early
• Engineering best practices reflect real production learnings and are widely adopted across the Mason team
• Delivery artifacts — runbooks, SOPs, reusable components — raise the bar for the whole team