Pune, Maharashtra, IN (On-site)1 open job
Rubiscape builds a unified enterprise Decision Intelligence platform from Pune, India, combining data engineering, ML, BI, IoT analytics, and AI copilots under a governance layer.
Rubiscape is an enterprise Decision Intelligence Platform founded in 2020 and based in Pune, India. The pitch: take raw enterprise data and turn it into autonomous, AI-powered decisions. What matters to anyone thinking about the trust boundary is where the company puts its weight - a single governed environment that consolidates data engineering, analytics, AI, and governance rather than scattering them across disconnected tooling. The stated goal is moving organizations from passive reporting to automated, outcome-driven action, with decisions that are informed, governed, and fast.
The platform ships as a set of studios inside one unified system. RubiFlow handles the data foundation and data engineering work; RubiStudio covers machine learning; RubiSight handles insights and BI; RubiAI is the AI decision copilot studio; RubiThings does IoT analytics; and RubiAdmin is the governance studio. That last one is the control plane for policy and oversight across the rest - the point where data lineage, access, and accountability are supposed to be enforced rather than reconstructed after the fact.
The technical surface spans decision intelligence, data engineering, analytics, artificial intelligence, machine learning, data governance, IoT analytics, business intelligence, and AI copilots. Customers are enterprise-scale - the company cites Fortune 500 enterprises - across banking, manufacturing, healthcare, and government, i.e. environments where data handling is regulated and the consequences of a bad automated decision are concrete. Reported scale markers include 8 international innovation patents, a claimed time to production-ready AI decisions of under 90 days, and a stated 5x reduction in total cost of ownership.
The operating thesis is straightforward: every enterprise decision should be informed, governed, and fast, and the tooling should reflect that instead of bolting governance on afterward. For engineering and security-adjacent talent, the work sits at the intersection of data pipeline integrity, model behavior, and policy enforcement across systems that ingest IoT telemetry, run ML, and drive AI copilots into live business processes.
