Full Stack Engineer

• 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.

• 3–5+ 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/Pulumi), 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.

Nice-to-have

• 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).