AI Native Full Stack Engineer - Technical Lead

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.