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
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