Developing Pathfinder, a multi-agent AI system built on protocol-driven, microservice-oriented architecture – where specialised agents are dynamically orchestrated to assist users in navigating the complexities of data integration, automating routine tasks and proactively solving problems to make them significantly more productive.
Defining and implementing scalable microservices that drive data mapping, transformation, and intelligent integration.
Developing and maintaining RESTful APIs to ensure seamless communication between services and systems.
Creating and optimising event-driven data pipelines to enable real-time processing, automation, and decision-making across our products.
Evaluating, selecting, and implementing the best-fit tools and technologies to solve complex engineering and data challenges effectively.
Collaborating directly with key customers and internal stakeholders to validate solution performance and ensure product impact and usability.
We’d love to meet someone with:
5+ years of professional software engineering with Python as your primary language, and a track record of building and running production-grade software – including being the person who fixed it when it broke.
Python craft: strictly typed, well-structured code that goes well beyond scripting or notebooks. You model your data at every boundary – Pydantic, attrs or similar – and you're comfortable working within an established framework.
A production mindset: you think about maintainability, testing, telemetry, and debugging, and you’re comfortable handling production issues and contributing to root cause analysis (RCA).
Deep, practical async Python: you have built services on asyncio and you know what it buys you and what it doesn't – async generators, cancellation, and why a single blocking call inside a coroutine stalls everything around it. Micro-services and event-driven architectures on top of that.
A feel for what goes wrong across a network boundary: timeouts, bounded retries with backoff, idempotency. You read an outbound call with no deadline on it as a defect rather than a detail.
Proficiency in designing and consuming RESTful APIs.
An affinity for AI/ML: curiosity about or hands-on experience with LLMs and agentic systems is a strong plus, though neither is required – we care more that you engineer well than that you already know this stack. Deep ML specialism is not needed.
Strong communication skills in English.
Nice to have:
Supply chain or logistics domain knowledge – the cherry on top, not a requirement.
Willingness to dig into the Java codebase of our core product to understand what functionality is already available, even before it’s exposed via an API.
Experience with CI/CD (GitHub Enterprise) and Infrastructure-as-Code (Terraform, Pulumi).
Familiarity with cloud AI services (e.g., SageMaker, Bedrock, Azure ML).
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