rippling
Full-Stack Software Engineer: ML Focus
Boston Bioprocess
Remote, IND
Full-time
Entry · 2+ years experience
Remote
USD 15000-30000 yearly / year
Discovered 2 weeks ago
FastAPIPyTorchAWSREST APIsCI/CDReact.js
Free
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FastAPIPyTorchAWS
Key Skills for This Role
FastAPIPyTorchAWSREST APIsCI/CDReact.js
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Key Responsibilities & Workflow
- Production ML: Design, develop, and own the production services that power our predictive, optimization, and generative AI applications using FastAPI, PyTorch, and AWS.
- MLOps & Infrastructure: Own deployment, retraining workflows, and establish best practices for versioning, CI/CD, observability, and drift monitoring. Be judicious in balancing supporting immediate needs with long-term infrastructure requirements.
- Engineering Rigor: Bring modular architecture, typed interfaces, and robust testing to early-stage ML code. Write clean, maintainable, and efficient code, taking ownership of the quality and performance of the ML systems you build.
- Backend & Data Connectivity: Create robust REST APIs, ETL pipelines, and human-in-the-loop feedback systems.
- Cross-Functional Collaboration: Work closely with data scientists and wet-lab scientists to translate scientific requirements into production constraints.
- Agile & AI-Assisted Development: Actively integrate AI-powered tools into your daily workflow to optimize and speed up tasks such as coding, refactoring, testing, and documentation. Effectively use Agile practices to plan and track your work.
Requirements
- Education & Experience: Master’s degree in Computer Science, Engineering, or a related field from a reputed US/European/APAC University with 2+ years of proven experience as a Full-Stack Developer (or similar role), OR a Bachelor’s degree from a Tier 1 Indian University with 5+ years of experience in a related field.
- Frontend Expertise: Deep expertise in building complex, UI-heavy, and responsive web applications using React.js, Next.js, Svelte, etc.
- Backend Knowledge: Strong knowledge of Python-based back-end languages such as FastAPI, Django, or similar.
- Data & Databases: Experience with databases like MySQL, PostgreSQL, or similar, including schema design for experimental or scientific data.
- ML in Production: Hands-on experience integrating ML frameworks (PyTorch, Jax, Tensorflow, or similar) into production software, with a clear understanding of how inference, batching, and model lifecycle behave under real load.
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