Lab49 is looking for Agentic Developers, engineers who don’t just use AI tools but write software through them.
This is not a role focused on “adding AI features” or “building AI platforms.” Instead, we are seeking developers who generate production‑grade enterprise code using large language models as a primary interface, particularly while modernizing or extending complex, legacy systems in highly regulated environments.
You will operate as a vibe coder in the best sense of the term: combining deep engineering intuition, strong system design instincts, and fluent command of LLMs to move faster than traditional development workflows- without compromising rigor, compliance, or reliability.
What You’ll Do
Generate, refactor, and extend enterprise‑grade codebases by orchestrating LLMs as first‑class development tools, not just assistants
Modernize and evolve legacy systems (often written in traditional enterprise stacks) using AI‑driven development workflows
Apply “vibe coding” practices responsibly within highly regulated domains such as financial services, capital markets, or other compliance‑heavy environments
Design and guide agentic workflows where LLMs reason, iterate, and produce code aligned with business and architectural constraints
Validate, test, and harden AI‑generated code to meet production, security, and audit standards
Collaborate closely with product managers, architects, and clients to translate ambiguous requirements into working systems - fast
Influence how Lab49 and its clients adopt LLM‑native software engineering practices at scale
What We’re Looking For
Strong software engineering background with experience in enterprise or legacy systems
Hands-on experience manufacturing production-grade software in AI-assisted way at scale (not just autocomplete or chat usage, but sustained code production through prompts, agents, or workflows)
Experience working within mature CI/CD environments, with automated testing fully integrated into the delivery pipeline
Comfort working in regulated, risk‑aware environments, where correctness, explainability, and traceability matter
Ability to reason about system behavior, edge cases, and failure modes- even when the code is AI‑generated
Fluency in at least one major enterprise language or ecosystem (e.g., Java, C#, Python, JVM‑based stacks, etc.)
Strong intuition for when to trust the model- and when not to
Leading teams through the AI adoption journey and helping enterprises build scalable, robust AI software factory frameworks
Nice to Have
Experience designing or using agentic development workflows (multi‑step prompting, tool‑using agents, code‑generation loops)
Exposure to modernization programs (monolith → services, legacy refactors, platform rewrites)
Opinions about how software engineering should evolve in an LLM‑first world - and the ability to defend them 
