Sand Tech Holdings Limited - Principal Software Engineer
Requirements
• 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. • How we work • How we work • 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.
Responsibilities
• 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. • Design, build, and evolve • Architect and develop scalable, production-grade distributed systems that operate across cloud-native, hybrid, sovereign cloud, and fully air-gapped environments. • Architect and develop • Own the full engineering lifecycle for platform components—from technical design and implementation through deployment, production operations, monitoring, and continuous improvement. • Own the full engineering lifecycle • Build robust data platforms capable of ingesting, transforming, and synchronizing data from modern cloud services, legacy systems, IoT devices, and disconnected operational environments. • Build robust data platforms • Develop backend services using Python and Node.js/TypeScript, designing APIs and distributed services that are secure, resilient, and performant. • Develop backend services • Deploy and operate AI-powered solutions, including machine learning and LLM-based systems, ensuring they are reliable, scalable, and production-ready. • Deploy and operate AI-powered solutions • Provide technical leadership by mentoring engineers, reviewing architecture and code, setting engineering standards, and guiding teams through complex technical decisions. • Provide technical leadership • Collaborate closely with product managers, engineers, and domain experts to translate complex operational challenges into elegant technical solutions. • Collaborate closely • Drive engineering excellence by improving platform reliability, observability, automation, testing, deployment practices, and overall system performance. • Drive engineering excellence • Embrace ambiguity and ownership, proactively creating clarity, defining technical direction, and delivering solutions to problems that don't come with predefined answers. • Embrace ambiguity and ownership • Leverage AI throughout the engineering lifecycle to accelerate development, improve productivity, and build intelligent systems without compromising engineering quality. • Leverage AI throughout the engineering lifecycle
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