Top Tech Jobs & Startup Jobs in Mumbai

Own and operate the AI-native platform runtime including Kubernetes (GKE), Terraform, GCP, CI/CD, observability, orchestration (Temporal, Kong). Improve reliability, performance, cost, multi-tenant provisioning, developer experience, and lead on-call and incident response.

Build intuitive, high-performance React interfaces for AI-powered insurance workflows. Collaborate with design and backend teams, create reusable UI components and design systems, integrate frontend features with AI services (document review, entity extraction, quote comparison), rapidly prototype, and iterate based on user feedback.

Lead the product roadmap for data ingestion, enrichment, and pipelines. Define integrations with PDFs, portals, APIs, spreadsheets, and carriers. Partner with AI and engineering to build scalable workflows for cleaning, validating, mapping, and enriching data. Work with customer-facing teams to remove adoption blockers, prioritize initiatives, prototype solutions, and ship frequent improvements.

Own internal IT systems: identity/SSO provisioning, device lifecycle and MDM/endpoint security, SaaS access and licensing, onboarding/offboarding, automation of IT workflows, internal networking, and support while contributing to security and compliance.

Own and operate the production infrastructure and delivery platform for AI products. Implement infrastructure-as-code, run Kubernetes, manage CI/CD, ensure security, reliability, observability, and compliance (single-tenant, SOC 2). Build automation and guardrails to enable safe, fast shipping.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Lead design, build, and operation of AI-powered product features across backend, frontend, and AI systems. Make architecture decisions, drive production deployments, improve code quality and reliability, mentor engineers, and own features end-to-end.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Provide technical leadership and hands-on engineering across AI, data, backend, and frontend systems. Drive architecture, remove technical risks, set engineering standards, mentor senior engineers, and influence cross-team technical direction.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.