About the Company
We are working with a US start-up that is building AI-driven workflow automation. The team is building an AI-first platform for the financial sector, focused on rethinking how investment professionals analyse data, make decisions, and execute workflows. At its core is a multi-agent system designed to turn complex, fragmented financial data into structured, actionable insight.
Backend engineers play a critical role in this vision, developing the infrastructure, services, and data systems that power intelligent, agent-driven workflows at scale.
Full-time roles are available with remote, hybrid, or on-site flexibility depending on preference and seniority. The office is in the Bay Area.
Candidates must be based in the US; there will be very occasional travel to the Bay Area.
What You'll Be Doing
- Design and build scalable backend services using Python and cloud-native technologies
- Develop APIs, orchestration layers, and data pipelines that enable real-time analytics and agent-based workflows
- Convert complex financial requirements into clean, efficient, and maintainable code
- Work closely with product, data, and ML teams to integrate LLMs and intelligent systems into user-facing workflows
- Take ownership of system performance, reliability, and long-term maintainability
- Contribute to architecture decisions and implement best practices across testing, DevOps, infrastructure-as-code, and observability
What They're Looking For
- 3--7 years of experience in backend engineering
- Proven track record of building and scaling production systems, particularly in data-heavy or real-time environments
- Strong Python skills and experience working with distributed systems
- A mindset geared toward building intelligent, autonomous systems rather than simple CRUD applications
- Experience designing and working with well-structured APIs (REST, GraphQL, or RPC)
- Familiarity with containerisation (Docker), orchestration (Kubernetes), and event-driven architectures (e.g. Kafka)
- Solid understanding of relational databases (e.g. PostgreSQL) and caching layers (e.g. Redis)
- Experience with performance optimisation, testing, CI/CD, and building secure, observable production systems
- Interest in complex problem-solving within domains such as fintech, trading, or enterprise SaaS
Nice to Have
- Experience with LLMs, AI frameworks, or agent-based systems (e.g. LangGraph, CrewAI, AutoGen, Haystack)
- Exposure to financial datasets, investment workflows, or alternative data pipelines
Perks
- Competitive compensation ($140 - $170K)
- Bonus
- Equity
- Unlimited PTO
- Access to AI assistants for work (coding and general-purpose tools)