Job Opportunities API

Architus specialises in connecting leading Fintech companies with top EU engineering talent, helping to build high-performing technology teams and establish a presence overseas. With offices in London, Vilnius and Kaunas, Architus has a strong track-record and impressive list of clients such as Invesco, Tide, Capital Group, Schroders, BNP Paribas, and HSBC. The Opportunity Architus is partnering with a leading company in AI-powered anti-money laundering and fraud detection. With over $100m in funding and clients including tier-1 banks, the company is growing fast and expanding internationally from its EU-based headquarters. Trusted by banks and payment providers worldwide, this organization combines traditional rules with explainable AI to detect financial crime more effectively while improving efficiency and reducing false positives. As a Senior Agentic Engineer at Hawk, your mission is to architect and build the agentic core of our AI Investigation Agent — a new product that fundamentally rethinks how human analysts collaborate with autonomous AI to investigate financial crime. You will own the hardest problems in the system: how the agent reasons, what context it sees, what tools it has, how we measure whether it's getting better or worse, and how we keep it reliable when it's making consequential decisions about real financial crime cases. You'll set the architectural direction for how Hawk builds agentic products, and you'll write the code that proves it works. About the role Architect and build the agentic systems behind the Investigation Agent, including LangGraph workflows, tool integration, memory, reasoning, and orchestration. Own context engineering: determine what information the agent sees, when it sees it, and how it's structured to maximize performance and reliability. Build feedback loops, evaluation frameworks, and tracing infrastructure (Langfuse and beyond) to make agent development measurable and iterative. Drive key architectural decisions around agent state, long-running workflows, failure recovery, deployment, rollbacks, latency, and cost at scale. Own complex backend engineering work end-to-end across our stack: Python, FastAPI, Postgres, Redis, Kubernetes, ensuring agent systems are reliable, observable, and production-ready. Partner with Product, UX, Data Science, and Engineering to shape solutions, provide technical guidance, and align features with the realities of current AI capabilities. Champion AI-native development practices, leveraging tools like Claude Code, Cursor, and similar coding agents while promoting automation and team-wide adoption. Help define Hawk’s core engineering standards for agentic products, including testing, evaluation, deployment, monitoring, and trustworthiness. What we are looking for Deep Python backend expertise and a track record of delivering production systems. Experience building agentic systems, including workflows, tool integration, context management, memory, and evaluation, using LangGraph, other frameworks, or custom solutions. Strong understanding of context engineering, prompt design, evaluation, and LLM failure modes, with a focus on building reliable systems at scale. Solid backend fundamentals across FastAPI (or similar), Postgres, Redis, Kubernetes, observability, and production operations. Daily user of agentic coding tools such as Claude Code, Cursor, or similar, with practical knowledge of their strengths, limitations, and best practices. Strong systems thinking and problem-solving skills, with the ability to shape product direction and architecture, not just execute requirements. Excellent communication and collaboration skills, able to clearly explain technical trade-offs to both technical and non-technical stakeholders. Nice to have Production experience with LangGraph (or equivalent agent orchestration frameworks) and Langfuse (or equivalent LLM observability/eval tooling). Prior experience in Anti-Financia