• Design and improve the platform systems that support model training, evaluation, and production serving. • Build infrastructure and tooling that make ML workloads more reliable, scalable, and cost-efficient. • Develop internal tools and workflows that are easy to operate both by humans and by agents. • Work on the architecture behind how models are deployed, served, and operated across research and product environments. • Improve how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure. • Develop internal tools and abstractions and agentic systems that reduce operational overhead for researchers and engineers. • Drive improvements across observability, automation, reliability, and developer experience. • Collaborate closely with researchers and product engineers to understand pain points and turn them into robust platform capabilities. • Contribute to technical direction and make pragmatic architectural tradeoffs as the platform grows.

• Implement fast, interactive Kanban boards with smooth drag-and-drop. • Build deal detail views, timelines, logs, and complex state-driven interfaces. • Create dashboards and analytics UIs with charts, KPIs, and real-time insights. • Architect shared UI components and frontend patterns for the CRM platform. • Build scalable state management solutions (Pinia/Vuex/Redux/Zustand). • Implement virtualisation, batching, and other performance-optimisation techniques. • Work with REST endpoints, real-time streams, and event-driven state updates. • Build resilient UI flows with strong handling of loading, error, and edge states. • Ensure correctness of pipeline and deal data across every interaction. • Participate in design and architecture reviews, RFCs, and long-term UI planning. • Collaborate deeply with Design and Product to shape intuitive, elegant interactions. • Mentor engineers on frontend patterns, state architecture, and performance tuning. • Own the reliability, latency, and overall quality of all Opportunities UI surfaces.

• Build the Analytics Platform (Greenfield): Design and build our first analytical data platform on GCP (warehouse, ingestion, transformation, and orchestration). • Construct Data Pipelines: Build reliable ELT/CDC pipelines from production Postgres and .NET/C# services using GCP-native tooling (Datastream, Dataflow, Pub/Sub) and dbt for transformation. • Establish Orchestration & CI/CD: Set up workflows using Cloud Composer/Airflow or Dagster, alongside data CI/CD environments and automated testing. • Model Data for AI & Analytics: Create dimensional models and a documented semantic/metrics layer to serve dashboards, data analysts, and internal Claude agents with unified definitions. • Develop the AI Data Layer: Build and maintain the retrieval substrate, embeddings pipelines, and vector storage (RAG data plumbing) to keep internal and external agents grounded in fresh data. • Own Governance & Compliance: Implement data validation, lineage, observability, and PII controls appropriate for sensitive financial, donor, payments, and cross-border tax data. • Collaborate Across Teams: Partner with the AI Systems Engineer and the .NET backend team to instrument product events and ship customer-facing insights.

• Design, build and maintain platform infrastructure using Infrastructure as Code (IaC) principles with tools such as Terraform. • Develop memory services, vector storage patterns, and semantic search capabilities. • Design secure access patterns for agents to governed enterprise data, including Databricks, and integrate guardrails for allowed tools and data sources. • Operate and evolve a Kubernetes-based platform for the Data & AI department, enabling deployment, management, and scaling of services following GitOps practices. • Build observability capabilities covering agent execution, performance, reliability, cost signals, and operational health. • Collaborate closely with other engineering, DevOps/MLOps, SRE, security, and product teams to define platform standards and improve developer self-service. • Participate in architecture discussions, propose improvements, and drive adoption of platform engineering best practices. • Stay current with emerging technologies in the cloud-native, AI platform, and agentic AI ecosystem to continuously improve the platform.