Senior Data Platform Engineer building and operating an integrated data platform for a fintech startup. Collaborating across teams to design scalable architecture in a competitive market.
About the role
- Senior AI Platform Engineer at Supernal building conversational AI systems for live business environments. Leading technical delivery of AI workflows on a proprietary platform in a remote global role.
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
Requirements
- 4+ years of experience as a software engineer, automation engineer, or systems builder shipping production systems
- 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.
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