Salary
≈ $26k – $58k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Middle · 3+ years exp
Overview
Company
Impact
Profile match
NeuroDiscovery is an AI-first OMICS platform leveraging an extensive longitudinal dataset in neurology, alongside research to discover cures for neurological conditions through advanced data-driven insights.
We are looking for a Full Stack Engineer who is comfortable shipping end-to-end from a polished React UI, through robust backend services, all the way to containerized deployments. You will work closely with product, design, and ML teams to turn ideas into reliable, production-grade software. If you enjoy owning features across the stack, care about clean code as much as user experience, and like working in a fast-moving environment, we'd love to talk.
Requirements:
- 3+ years of professional experience building production web applications.
- Strong proficiency in React with TypeScript and modern JavaScript (ES6+).
- Hands-on experience with micro-frontend architecture (Module Federation, single-spa, or similar) including versioning, shared dependencies, and deployment trade-offs.
- Backend experience in at least one of Go, Python, or Java, with working familiarity in a second.
- Solid understanding of HTTP, REST, authentication patterns, and relational / NoSQL data stores.
- Working knowledge of Docker writing Dockerfiles, multi-stage builds, and local container workflows.
- Basic working knowledge of Kubernetes deployments, services, configmaps / secrets, and reading logs / events to debug.
- Comfort with Git, CI/CD pipelines, and writing automated tests.
Nice-to-have:
- Experience with state management libraries (Redux, Zustand, React Query) and modern build tools (Vite, Webpack 5 Turbopack).
- Exposure to message queues (Kafka, RabbitMQ, NATS) or event-driven architectures.
- Familiarity with observability tooling Prometheus, Grafana, OpenTelemetry, structured logging.
- Cloud experience on AWS, GCP, or Azure.
- Experience working alongside ML / AI teams or integrating model inference into product surfaces.
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