We're looking for a senior engineer to help shape and lead the platform that other engineering teams build on top of. Our mission is to make AI agents and high-throughput backend systems easy for internal teams to adopt, and you'll own significant parts of how that platform is designed: the reusable libraries, Spring Boot starters, templates, and supporting tooling that the whole company depends on.
You'll work across two worlds:
Agentic frameworks: the libraries, starters, and orchestration patterns that internal teams use to ship LLM-powered agents (Python/LangChain/LangGraph on the agent side, Kotlin/Spring Boot on the platform side), plus the supporting frameworks (evaluations, guardrails) that prove those agents actually work.
Core backend platform: Kotlin services and libraries and the shared building blocks other teams depend on.
This is a high-leverage, builder-and-multiplier role. You'll make foundational design decisions, set the patterns and standards other engineers follow, and raise the bar for everyone building on the platform. Because the foundations you create sit underneath many teams' work, understanding how the company fits together, its products, domains, and the teams behind them is central to doing it well. While AI development experience is a great plus, we are looking for someone with genuine enthusiasm for the field, someone ready to dive deep into agentic systems and become our go-to expert.
What You'll DoOwn the design of shared libraries, Spring Boot starters, and templates while making the architecture, API, and trade-off decisions that determine how easily internal teams ship on top of them.
Set the patterns and standards for our agentic frameworks, agent orchestration, tool use, memory, and the surrounding plumbing (message routing, human-in-the-loop workflows, MCP integration), and bring teams along with them.
Mentor and upskill product teams in Python-based AI agent development, including hands-on guidance with the LangChain and LangGraph frameworks.
Lead the evaluation and guardrail strategy so we can measure agent quality, reliability, and regressions.
Drive technical work across our Kotlin/Spring Boot platform and the client libraries that wrap it.
Build and maintain a clear map of the wider ecosystem (how our systems, domains, and teams connect) and use it to make foundational decisions that fit real needs across the company.
Drive adoption and multiply other engineers. Mentor teammates, review designs and code, run the docs/examples that turn a good framework into one that teams actually choose, and feed what you learn back into the platform.
Set the research direction in unfamiliar territory: identify where to invest, prototype the promising approaches, and turn the winners into production-ready foundations the rest of the org can rely on.
Raise the bar on developer experience, clear APIs, good defaults, useful errors, and documentation people actually read, and hold the platform to it.
7+ years of professional software engineering experience, including a track record of building libraries, frameworks, or platform components that other engineers built on.
Deep experience with Kotlin / the JVM and Spring Boot, and strong instincts for API and library design. You know what makes a foundation pleasant to build on versus painful.
Demonstrated technical leadership: you set direction, make and justify architectural trade-offs, and lift the engineers around you, with or without formal authority.
Genuine drive to lead on agentic AI and its surrounding tooling (frameworks, evaluation, guardrails, orchestration), and to become a go-to expert for the org.
The judgment to drive ambiguity to clarity: to take an open-ended or unfamiliar problem and turn it into a sound, well-scoped design others can execute.
A systems thinker who owns the bigger picture: you actively understand how the company's products, domains, and teams connect, and design for the whole, not just the module in front of you.
Excellent communication and a self-sufficient way of gathering context: you do your homework, ask sharp questions of the right people, and can explain and sell technical directions to both engineers and stakeholders.
Pragmatism about Python: Working knowledge of Python: You don’t need an extensive background in managing large-scale Python production environments, but you should be comfortable with the language. Since our tooling relies on Python/LangChain/LangGraph, we look for the ability to ramp up quickly and act as a senior lead across both our Kotlin and Python stacks
Strong distributed systems background like messaging/queues (SQS, Redis), gRPC, event-driven architectures, including the failure modes and operational realities.
Cloud experience (AWS preferred) and infrastructure-as-code.
Experience growing a platform/DevEx practice by establishing standards, golden paths, and adoption across multiple teams.
Contributions to or maintenance of open-source libraries or frameworks.
Hands-on experience with LLMs, LangChain/LangGraph, or other agent frameworks.
Experience defining evaluation / experiment-tracking / observability strategy for ML or agent systems (e.g. Langfuse, MLflow, or similar).
