CapIntel

Backend Engineer

• Design, build, and maintain robust and scalable data pipelines using TypeScript, Node.js, Temporal.io, and NoSQL databases such as MongoDB.
• Architect storage solutions for structured and unstructured financial data, ensuring performance, reliability, and scalability.
• Identify and drive performance optimizations across existing pipelines — profiling bottlenecks, improving throughput, and reducing latency at scale.
• Deploy, monitor, and optimize services on AWS, applying best practices for observability, cost efficiency, and fault tolerance.
• Drive technical excellence by writing clean, maintainable code supported by thorough unit and integration testing.
• Participate in and elevate code reviews, architecture discussions, and technical planning, setting a high bar for engineering quality.
• Contribute to the evolution of our backend development standards and processes.
• Partner closely with product, platform, and design teams to translate business requirements into scalable backend solutions.
• Support the development and maintenance of internal APIs where needed, ensuring reliability and consistency across integrations.

Senior Backend Developer, Data Team

Engineer

• Design and implement LLM-powered features into our core application via model APIs (e.g. Anthropic, OpenAI, Cohere), with a focus on reliability and production-readiness
• Architect and maintain retrieval-augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources
• Manage context window strategy, determining what information enters the model, when, in what format, and at what level of compression to optimise for accuracy, cost, and latency
• Design and implement agentic workflows enabling the platform to handle multi-step, autonomous tasks
• Build guardrail and output validation layers that constrain model behaviour and ensure AI features act within well-defined, compliant boundaries
• Develop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independently
• Build evaluation frameworks to measure context effectiveness, output quality, and agent reliability in production
• Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles
• Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specifications
• Act as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practices

Senior Backend Developer, Data Team

Context Engineer