Infinity Constellation
Senior AI Engineer (Clients) - Supernal
1mo ago
SeniorRemotecruddata modelsai systemsconversational aiintegrationworkflow management
Senior AI Engineer responsible for building production software powering AI Employees deployed in business environments, managing client delivery and technical ownership.
Responsibilities
- As a Senior AI Platform Engineer, you'll be on the frontlines of our most critical customer implementations, building the production software that powers AI Employees deployed in real business environments.
- You'll design, build, and deliver the core software foundations — services, data models, and CRUD applications — plus reliable integrations with external systems. On top of that foundation, you'll build agentic and conversational AI systems that handle live users, multi-turn conversations, real-time constraints, and complex workflows. 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 building, debugging, and shipping, while raising the engineering bar through crisp technical judgment and strong ownership.
- Build production software with code and Supernal's proprietary platform, including backend services, data models, and CRUD applications
- Build and maintain integrations with external systems (APIs, webhooks, third-party tools, and data sources) that AI Employees can safely act on
- 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 application design, API integrations, LLM orchestration, RAG design, 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 system 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
Other
- Location: Remote (Global)
- Reports to: Director of Engineering
- Company: Supernal
- Type: Contractor or EOR FTE
- Rate: $35-50/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.
- Have 4+ years of experience as a software engineer, automation engineer, or systems builder shipping production systems
- 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
- Have hands-on experience deploying voice agents using leading platforms (e.g., ElevenLabs, Retell, Nextiva), including telephony and streaming audio integration patterns
- 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 and APIs, and have shipped real integrations in production
- 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
- Provide your own computer with reliable, high-speed internet. Be willing to work in Americas time zones.
- 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