Senior AI Platform Engineer - Supernal

Department: Supernal

Location: Remote

Compensation: $50 per hour • All contractors have a three month trial period at $40/hr

Employment Type: FullTime

Location: Remote (Global)

Reports to: Head of Product

Company: Supernal

Type: EOR FTE or Contractor

Rate: $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, with orchestration powered by N8n. 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 voice-first and 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 be responsible for translating customer requirements and SOWs into working systems, owning delivery timelines, managing technical tradeoffs, and ensuring 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 voice and 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

• 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 experience deploying voice agents using leading voice platforms (e.g., ElevenLabs, Retell, Nextiva, etc), including telephony + streaming audio integration patterns

• Have hands-on experience building and deploying conversational or voice-based AI systems used by real users

• Are comfortable owning delivery outcomes, 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)

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

• Communicate clearly with both technical and non-technical stakeholders, including in client-facing contexts

• Thrive in fast-paced, ambiguous startup environments and take ownership without needing a heavy process

• 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

• Engineering best practices reflect real production learnings and are widely adopted across the Mason team