Senior AI Platform Engineer - Supernal

Location: Remote (Global)

Reports to: Director of Engineering

Company: Supernal

Type: EOR FTE or Contractor

Provide your own computer with reliable, high-speed internet.

Be willing to work in Americas time zones.

Rate: $40 - $50/hr

At Supernal, we help SMBs hire their first AI employee. Our AI teammates are built with intelligent, agentic workflows and deployed on our proprietary platform. We don't build tools — we deliver working, value-generating AI Employees.

Our AI Platform Engineers, known internally as Masons, are the builders behind these systems. Now, we're looking for a Senior Mason to help lead this craft.

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.

• 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

• Have 4+ years of experience as a software engineer, automation engineer, or systems builder shipping production systems

• Have hands-on experience deploying voice agents using leading platforms (e.g., ElevenLabs, Retell, Nextiva), including telephony and streaming audio integration patterns

• Understand multi-turn conversation design: state management, context windows, interruption handling, and graceful recovery

• Have tackled real-time constraints in production: latency budgets, streaming audio, fallback paths, and API chaos

• Write automated tests as a matter of course — unit tests, integration tests, and end-to-end workflow validation — and treat testing as part of shipping, not an afterthought

• Apply solid engineering fundamentals: error handling, retry strategies, separation of concerns, and clean interfaces between components

• Are comfortable owning delivery outcomes end-to-end — not just writing code — including timelines, reliability, and client success

• Have deep experience with agentic architectures, workflow automation platforms (n8n, Zapier, Make), and APIs

• Understand LLM orchestration, prompt engineering, function calling, and retrieval-augmented generation (RAG)

• Can prototype fast and finish the job to production quality — with tests, error handling, monitoring, and runbooks

• Are an elite debugger who can reason through edge cases, flaky agents, and real-world API failures

• Communicate clearly and fluently in English — both in writing and verbally — especially when articulating technical decisions, tradeoffs, and implementation choices to technical and non-technical stakeholders alike

• Can run meetings, drive decisions, write crisp updates, and ask the right questions early — without needing heavy process

• Thrive in fast-paced, ambiguous startup environments and take ownership without being asked

• Bring a low-ego, high-integrity approach to collaboration and leadership

• 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