Senior AI Platform Engineer - Supernal

Описание вакансии

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