• Work on a product that helps companies navigate complex government data, from contracts and grants to market signals and turn it into actionable insights using AI. • Collaborate closely with a small, senior team and have real ownership, from shaping solutions to shipping them to production and improving them over time.

• Serve as technology lead and own key decisions including the tech stack and strategy for backend design, data infrastructure, and machine learning investments. • Work directly with product, design, and other engineers to build out our platform. • Help establish and grow our engineering organization and mentor the next generation of technology leaders.

• Conceiving, designing, building, and launching new user-facing features • Implementing new AI features to turbocharge our customers’ daily tasks • Improving the performance and scalability of our GraphQL and REST API • Improving how we sync millions of sales emails and calendar events each month • Working with Twilio's API, WebSockets, and WebRTC to improve our calling features • Building user-facing analytics features that provide actionable insights based on sales activity data • Improving our Elasticsearch-backed powerful search features • Improving our internal messaging infrastructure using streaming technologies like Kafka and Redis • Building new and enhancing existing integrations with other SaaS platforms like Google’s G Suite, Zapier, and web conferencing providers • Enhancing our web app with real-time updates powered by GraphQL Subscriptions • Leveraging the latest AI LLMs from providers like OpenAI, Groq and AssemblyAI

• Write Python applications that are resilient, robust, and integrate well with other apps in a service architecture. • Furthering Developer Experience (DevEx) by mentoring others in writing code that is intuitive, clear, and easy to test • Developing observability for new and existing ML applications and GenAI/LLM integrations, making use of the Grafana Stack (Prometheus, Loki, Tempo) • Develop integrations and services that communicate with Google Services. • Working closely with Data Scientists and ML Engineers throughout the lifecycle of productionising their models • Being responsive to incidents regarding ML applications - including an understanding of how to triage and resolve issues relating to CPU, memory, and GPU utilisation • Documenting procedures and guides to facilitate knowledge sharing and help other engineers level up through pairing and mentoring • Participating in hiring and onboarding new team members; taking on end-to-end project management responsibilities as we grow.