CapIntel

• 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.

• 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

• Lead client onboarding: Gather requirements, understand client goals, and design tailored solutions using existing features or enhancements. • Manage implementation lifecycle: Oversee end-to-end onboarding with developers and designers, removing bottlenecks and ensuring timely, seamless delivery. • Serve as client liaison: Act as the primary contact during onboarding, managing communication, requirements, and escalations, while partnering with Customer Success Managers to strengthen relationships. • Own project scope: Manage contracts (SOWs, Order Forms) and ensure enterprise configurations integrate successfully with client systems. • Advance platform usability: Partner with product teams to identify and implement UX/UI improvements informed by client feedback and usage metrics. • Drive continuous improvement: Champion enhancements that align the platform with evolving client and business needs.

• Lead client onboarding: Gather requirements, understand client goals, and design tailored solutions using existing features or enhancements. • Manage implementation lifecycle: Oversee end-to-end onboarding with developers and designers • Serve as client liaison: Act as the primary contact during onboarding, managing communication, requirements, and escalations. • Own project scope: Manage contracts (SOWs, Order Forms) and ensure enterprise configurations integrate successfully • Advance platform usability: Partner with product teams to identify and implement UX/UI improvements • Drive continuous improvement: Champion enhancements that align the platform with evolving client and business needs.

• Own the strategy, roadmap, and outcomes for Flow end-to-end. • Design onboarding experiences that drive faster activation, stronger adoption, and long-term customer confidence. • Mentor PMs and help raise the bar for product thinking, execution, and customer empathy across the team. • Talk to advisors, enterprise partners, and internal teams regularly to uncover friction points and shape product direction. • Partner closely with design and engineering to move from concept to launched product quickly and thoughtfully. • Lead cross-functional launches with Sales, Marketing, and Customer Success, using feedback and data to continuously improve the experience. • Define clear success metrics and use data to guide prioritization and product decisions.

• 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

• Build and maintain backend services and integrations that support client onboarding • Implement client-specific backend solutions with a focus on reliability and maintainability • Support integrations with client systems and assist in resolving technical issues • Collaborate with Product, Implementation, and Data teams to deliver projects end-to-end • Contribute to improving internal tooling, integration patterns, and team processes • Participate in code reviews and collaborate with teammates to maintain high code quality • Debug, troubleshoot, and resolve issues across backend services and integrations