Top Tech Jobs & Startup Jobs

Architect and build core Decision Intelligence platform capabilities: heterogeneous data integration, semantic modeling, simulation and optimization services, scalable distributed backend systems, AI/ML and LLM-powered production deployments, data pipelines, and operational tooling. Provide technical leadership, own full lifecycle from design to production, improve reliability, observability, and drive engineering excellence.

The Business Analyst drives requirements and analysis throughout delivery, manages stakeholder relationships, produces documentation, leads testing, and measures value while mentoring junior teammates.

Design, build and maintain full-stack, production-grade applications and APIs on Azure to serve AI/model outputs. Implement CI/CD, automated testing, observability and secure integrations, mentor mid-level engineers, and support deployment and user acceptance for operator-facing situational-awareness dashboards for utilities.

Drive the adoption of AI platforms and solutions in public sectors, ensuring stakeholder engagement, operational integration, and successful adoption strategies.

Lead end-to-end product design for AI-enabled, data-heavy products: conduct user research, design complex UX/UI and interaction details, build scalable design systems, collaborate with product, engineering and data science, mentor designers, and champion user-centered practices across the organisation.

Own the vision and roadmap for the core developer and AI platform. Dogfood APIs, deploy and prototype with LLMs and AI agents, maintain reference apps and IaC templates, run user interviews, define PRDs and success criteria, align with engineering and client deployments, and represent the product externally. High travel (50–75% EMEA).

Design, develop and deploy machine learning and time-series models for water, wastewater and infrastructure systems; work with operational telemetry (SCADA/AMI/IoT), geospatial and graph analytics; integrate models into production pipelines with MLOps and monitoring; communicate results to stakeholders; perform Physical AI research and mentor junior data scientists.

Design, build, and maintain scalable cloud-native data platforms and pipelines (batch, streaming, near-real-time). Implement distributed processing (Spark), event-driven systems (Kafka), governance, security, observability, and support ML/analytics. Partner with cross-functional teams, optimize performance/cost, and mentor engineers while enabling production-grade data products.

Lead end-to-end test automation: design manual test cases from user stories, convert them into Playwright+TypeScript automated regression suites, test GraphQL APIs, maintain CI/CD integration, and improve automation coverage and test quality across web applications.