Top Tech Jobs & Startup Jobs

Build and operate Rubiscape’s AI platform infrastructure, including LLM gateways, RAG pipelines, prompt management, vector databases, inference APIs, guardrails, model serving, and observability. Integrate open-source and hosted models across SaaS, BYOC, and air-gapped deployments while supporting multi-tenant security, audit logging, usage metering, and regulated-sector requirements.

Serve as the technical bridge between prospects and Rubiscape’s Decision Intelligence Platform. Lead discovery, deliver tailored demonstrations, build proof-of-concept environments, develop ROI models, respond to RFPs and security questionnaires, and collaborate with Product and Engineering. The role requires translating data, analytics, and AI capabilities into business value for enterprise customers across multiple industries and deployment models.

Enterprise Account Executive responsible for acquiring new enterprise customers and expanding platform adoption across assigned Indian accounts. Owns the full sales cycle, including prospecting, discovery, solution workshops, proof-of-concept scoping, business-case development, forecasting, negotiation, and closing. Builds relationships with executive and technical buying stakeholders, partners with solution engineers, maintains pipeline coverage, and contributes to territory planning and competitive intelligence. Sells data, analytics, AI/ML, BI, and cloud platform capabilities to large enterprises.

Lead enterprise B2B SaaS sales for Rubiscape’s data science and decision intelligence platform. Responsibilities include prospecting global capability centers, large enterprises, and public sector organizations; managing full sales cycles; closing high-value ARR contracts; communicating platform architecture and total-cost advantages; and partnering with technical pre-sales teams to align customer needs with Rubiscape’s lifecycle engines.

Design and operate production MLOps infrastructure for large-scale model training, deployment, monitoring, and rollback across SaaS, cloud, on-premises, and air-gapped environments. Build ML pipelines, model registries, containerized serving platforms, observability systems, and governance controls. Collaborate with engineering, security, compliance, and customer teams while managing production model reliability, performance SLOs, and incident response.

Designs and maintains automated and manual testing frameworks for web interfaces, microservices, data pipelines, IoT streaming ingestion, and enterprise application layers. Performs data validation, regression, performance, and load testing across cloud and on-premises environments. Documents software defects, collaborates with developers, and supports reliable releases for a B2B SaaS or data platform.

Design, build, and own distributed backend microservices supporting data ingestion, ML pipeline orchestration, model serving, and real-time analytics. Develop Kafka streaming solutions, PostgreSQL schemas, REST and gRPC APIs, Redis caching, and service observability. Deploy and debug Kubernetes services, implement security controls, participate in on-call rotations, and improve reliability. The role requires expertise in distributed systems, database optimization, microservice patterns, and production-grade backend engineering using Node.js, Python, or Go.

Own territory revenue and pipeline generation through authorized resellers, regional systems integrators, and cloud marketplace partners. Manage the full enterprise sales cycle, including prospecting, discovery, demonstrations, proof of concepts, pricing, forecasting, and closing. Enable partners, maintain deal registration and margin structures, engage senior data and technology stakeholders, and expand existing accounts through cross-sell and upsell opportunities.

Own post-sale relationships for enterprise and institutional clients, leading onboarding, adoption, retention, platform democratization, and expansion. Advise customers on developing internal AI-skilling academies using the Rubiversity framework. Monitor platform health metrics, remove adoption barriers, and identify renewal and account-expansion opportunities. The role requires enterprise B2B SaaS or data analytics experience and the ability to discuss data workflows with data science and IT teams.

Develops production-grade data science solutions using statistical modeling, machine learning, causal inference, and experimental design. Responsibilities include data exploration, feature engineering, model validation, deployment packaging, explainability, model documentation, experiment tracking, and production pipeline collaboration. The role focuses on fraud and risk, predictive maintenance, and patient outcome modeling, while mentoring junior analysts and translating research into platform capabilities.