AI Native Fullstack Engineer/Technical Lead
We're hiring an AI-native Fullstack Engineer / Technical Lead to help transform how we build software, this is not “engineering with some AI bolted on".
You're a working technical lead. You write production code, shape architecture alongside our senior engineers, and set the quality bar. Your highest leverage, though, is pulling the rest of the team into AI-native development — proving what's possible with agentic tooling, codifying the patterns that work, and refusing to let the team default back to old habits.What You'll Own
- Ship at exponential pace. Build full-stack features end-to-end using agentic development workflows. Default mode is ship-to-learn behind feature flags — not month-long spec cycles.
- Pull the team forward on AI. You are the team's accelerant for agentic development. Demonstrate, codify, and evangelize the workflows that turn a two-week feature into a two-day feature.
- Architect for agents. Shape full-stack systems where agents are first-class components — planning loops, tool use, memory, evaluation. Make pragmatic calls on quality, cost, and latency.
- Set the engineering bar. Drive code quality, testing, observability, and security — at AI-native speed, never as a brake.
- Ship LLM-powered features responsibly. RAG pipelines, tool-use integrations, evaluation harnesses. Manage hallucination, prompt injection, and runaway cost.
- Mentor and unblock. Coach through code review, pairing, and design reviews. Remove obstacles so the team can ship.
- Own production reliability. Lead incident response and drive blameless postmortems.
What We're Looking For
AI-Native Mindset (this is the filter)
- You use Claude Code, Cursor, Codex, or equivalent agentic tools every day in production work — not as an experiment, not as a side toy.
- Strong opinions on what to delegate to agents, what to verify, and where humans still need to drive.
- You've shipped LLM-powered features in production: prompting, structured output, tool/function calling, streaming.
- Hands-on experience designing agentic systems: planning loops, tool use, memory, multi-step execution, evaluation.
- Familiarity with RAG, embeddings, and vector stores (Azure AI Search).
- Working knowledge of LLM evaluation: offline evals, golden datasets, LLM-as-judge, guardrails, prompt injection mitigation.
- Voracious curiosity. You track the frontier — papers, model releases, new tools — and pull what actually works back to the team.
Engineering Foundation
- 7+ years of professional software engineering, including 2+ years as a technical lead or leading teams. Shorter timeline is fine if you've shipped harder things faster than that implies.
- Strong CS fundamentals: data structures, algorithms, concurrency, distributed systems.
- Deep experience in a modern back-end stack: C#/.NET 8+, TypeScript/Node, Go, Python, Java, or Kotlin. REST and gRPC API design.
- Proficiency with a modern front-end framework (React, Next.js, Angular, or Vue) plus TypeScript.
- Strong SQL (Postgres, SQL Server, or MySQL) plus a non-relational or event store (Redis, Mongo, DynamoDB, Kafka).
- Cloud-native delivery on Azure, AWS, or GCP. Containers, orchestration, and IaC (Terraform, Bicep, or Pulumi).
- CI/CD discipline (Azure DevOps, GitHub Actions). Git workflows with disciplined review.
- Observability (OpenTelemetry, Datadog, App Insights) and secure-by-default development.
- Track record of shipping and operating non-trivial production systems end-to-end.
Leadership
- You lead by demonstration. You don't need formal authority to move a team forward.
- Excellent written and verbal communication. You can explain a tradeoff to a junior engineer, a product partner, or a CEO.
- You recognize passive compliance for what it is — and push past it.
Nice to Have
- Experience operating multi-agent systems in production (LangGraph, Semantic Kernel, AutoGen, custom runtimes).
- Experience with agent-to-agent protocols, orchestration patterns, memory/state management, and tool registries at scale.
- Event-driven architectures (Kafka, Event Hubs, NATS).
- Model fine-tuning, distillation, or self-hosted inference.
- Background in B2B SaaS, procurement, supply chain, or the building trades.
- Open-source contributions, technical writing, or conference talks on AI-native engineering.
