JOB SUMMARY: As a Senior Full Stack Engineer, your primary responsibilities will include designing, developing, and maintaining frontend and backend services for a multi-tenant SaaS platform.
RESPONSIBILITIES: - Design, develop and maintain frontend and backend services for a multi-tenant SaaS platform.
- Build intuitive job/pipeline submission UIs (including no-code/low-code pipeline builders) and programmatic APIs.
- Integrate and operationalize AI/ML models and GPU-backed workloads (containerized model images, model versioning, result provenance).
- Implement asynchronous job orchestration, batch processing, queueing, retries and autoscaling.
- Ensure secure data handling, encryption, access control and tenant isolation.
- Develop and document REST/GraphQL APIs consumed by internal and external clients.
- Implement CI/CD, observability (logs/metrics/tracing), and automated testing for reliability.
- Troubleshoot production issues and optimize performance and cost of cloud resources.
- Collaborate with cross-functional teams to gather requirements, shape product features, and deliver customer-facing functionality.
REQUIREMENTS: - 7+ years professional full-stack experience building production web applications.
- Strong frontend skills with React and Next.js (TypeScript preferred).
- Strong backend skills in Python (FastAPI) and/or Node.js/NestJS; comfortable designing REST/GraphQL APIs.
- Practical experience integrating ML or compute-heavy services into applications (model serving, job orchestration, or similar).
- Experience with containerization (Docker) and orchestration (Kubernetes / EKS / AWS Batch).
- Cloud experience (AWS/GCP/Azure) — provisioning, IAM, S3, autoscaling.
- Familiarity with databases (SQL and NoSQL), S3-style object storage, and designing data models for jobs/results.
- Experience building or shipping no-code/low-code user interfaces, pipeline editors, or drag-and-drop workflows.
- Knowledge of CI/CD pipelines, infrastructure-as-code (Terraform), and monitoring/alerting tools.
- Strong engineering fundamentals: testing, observability, security best practices, and performance optimization.
- Excellent communication skills in English and ability to collaborate with researchers and stakeholders.
- Hands-on experience with MLOps tooling (MLflow, Argo, Kubeflow, Step Functions).
- Experience with GPU compute stacks, CUDA, Conda, or packaging complex scientific toolchains.
- Background working with scientific users (bioinformatics, computational chemistry, or similar).
- Prior experience in multi-tenant SaaS or enterprise security (SSO/SAML, data isolation).