Looking for engineering problems that actually matter? Join Sand's Decision Intelligence Platform team and help build the semantic engines, data platforms, and AI-powered systems that enable governments and enterprises to make better operational decisions. This isn't feature development—it's designing the core technology behind critical infrastructure used by millions of people.
Sand Technologies is a global Physical AI company using data and AI to make critical industries work better. We partner with governments, cities and enterprises to improve how essential systems operate across healthcare, water, energy, telecommunications and infrastructure.
Our work delivers proven real-world impact. We have built AI systems that help manage London's water supply, supported telecom network planning across hundreds of cities, and developed digital healthcare platforms serving tens of millions of people across Africa. From intelligent command centers to AI-powered infrastructure platforms, we help organizations sense, analyze and act in complex environments.
Our people are ambitious, curious and relentlessly practical. Our teams work alongside clients in the field, solving hard problems and deploying solutions that last. With colleagues across Africa, Europe, the UK and the US, we operate across the full stack - from research and engineering to deployment and capability building.
Our mission is simple: to harness AI to solve humanity's most pressing challenges.
We're looking for a Staff / Principal Software Engineer to help build the next generation of Sand's Decision Intelligence Platform—the technology powering intelligent infrastructure for governments and enterprises across healthcare, water, telecommunications, energy, and other critical industries.
This isn't a traditional software engineering role. You'll architect and build the core capabilities that allow operators to understand complex systems, model how they behave, predict what happens next, and make better operational decisions in real time. From heterogeneous data integration and semantic modeling to distributed services, simulation engines, and AI-powered workflows, you'll own platform capabilities from concept through production.
Our platform is already solving real-world problems at national scale. Now we're looking for engineers who enjoy creating clarity from ambiguity, solving difficult systems problems, and building products where reliability matters.
- Design, build, and evolve core platform capabilities, including heterogeneous data integration, ontology and semantic modeling engines, simulation services, optimization capabilities, and AI-powered decision support systems.
- Architect and develop scalable, production-grade distributed systems that operate across cloud-native, hybrid, sovereign cloud, and fully air-gapped environments.
- Own the full engineering lifecycle for platform components—from technical design and implementation through deployment, production operations, monitoring, and continuous improvement.
- Build robust data platforms capable of ingesting, transforming, and synchronizing data from modern cloud services, legacy systems, IoT devices, and disconnected operational environments.
- Develop backend services using Python and Node.js/TypeScript, designing APIs and distributed services that are secure, resilient, and performant.
- Deploy and operate AI-powered solutions, including machine learning and LLM-based systems, ensuring they are reliable, scalable, and production-ready.
- Provide technical leadership by mentoring engineers, reviewing architecture and code, setting engineering standards, and guiding teams through complex technical decisions.
- Collaborate closely with product managers, engineers, and domain experts to translate complex operational challenges into elegant technical solutions.
- Drive engineering excellence by improving platform reliability, observability, automation, testing, deployment practices, and overall system performance.
- Embrace ambiguity and ownership, proactively creating clarity, defining technical direction, and delivering solutions to problems that don't come with predefined answers.
- Leverage AI throughout the engineering lifecycle to accelerate development, improve productivity, and build intelligent systems without compromising engineering quality.
- 10+ years of software engineering experience building, shipping, and operating production systems across multiple technology stacks and domains.
- Strong infrastructure engineering expertise, with hands-on experience in Kubernetes, containers, networking, Infrastructure as Code (IaC), and production environments across AWS, Azure, and/or on-premises infrastructure.
- Deep backend engineering experience using Python and/or Node.js/TypeScript, with a strong understanding of distributed systems, APIs, relational databases, and scalable application design.
- Proven data engineering capabilities, including building data pipelines, streaming architectures, orchestration workflows, and integrating heterogeneous enterprise data at scale.
- Experience deploying and operating AI/ML systems, including machine learning models and LLM-powered applications in production, with an understanding of MLOps principles.
- Exceptional systems design skills, with a track record of architecting reliable, scalable, and resilient platforms that solve complex real-world problems.
- A technical leader who leads by example, equally comfortable mentoring engineers, driving architectural decisions, reviewing code, and contributing hands-on to implementation.
- Comfortable working across the stack, with enough frontend experience to design, review, and contribute when needed, while bringing deep expertise across backend and infrastructure.
- Highly self-directed and outcome-oriented, able to navigate ambiguity, define technical direction, and drive complex initiatives with minimal oversight.
- A fast learner who quickly adapts to new technologies, domains, and constraints, and enjoys solving problems that don't have obvious solutions.
- An ownership mindset, taking responsibility for the long-term success of the systems you build, from initial design through production support and continuous improvement.
- An AI-first engineer, comfortable leveraging AI-assisted development tools to accelerate delivery while maintaining high standards for software quality, architecture, and engineering excellence.
- Experience building semantic data models, ontologies, or knowledge graph platforms.
- Experience with simulation, optimization, or digital twin technologies.
- Experience developing AI agents or autonomous workflows.
- Exposure to healthcare, utilities, telecommunications, energy, or government technology platforms.
- Experience building platforms for sovereign cloud or air-gapped deployments.
- Experience contributing to platform engineering or developer tooling initiatives.
Due to the highly collaborative and internationally distributed nature of our work, successful candidates must be comfortable operating in small teams while contributing to larger, globally coordinated efforts. A strong sense of ownership, self-motivation and discipline in maintaining clear and consistent communication through virtual collaboration tools and video conferencing is essential.
