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
About Supernal
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.
The Role
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
You Might Be a Fit If You...
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
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