onsite
AI/ML Infrastructure Engineer - Manifold Bio
Devops Engineer
Build and scale AI/ML pipelines for protein design, leveraging Python, AWS, Kubernetes, and Docker to process massive in‑vivo datasets and support computational models that accelerate drug discovery.
About the role
Key Responsibilities
- Design, develop, and maintain scalable data pipelines for processing large protein‑design datasets using Python and AWS services.
- Implement containerized microservices with Docker and orchestrate them on Kubernetes clusters to ensure high availability and performance.
- Collaborate with computational scientists to integrate machine‑learning models into the production workflow, optimizing inference latency and throughput.
- Monitor system health, troubleshoot performance bottlenecks, and continuously improve infrastructure reliability.
- Document architecture, best practices, and operational procedures for cross‑team knowledge sharing.
Requirements
- 3+ years of experience building production ML pipelines in a cloud environment.
- Proficiency in Python, AWS (S3, EC2, Lambda, SageMaker), Kubernetes, and Docker.
- Strong understanding of bioinformatics data formats and large‑scale data processing.
- Experience with CI/CD, monitoring, and logging tools (Prometheus, Grafana, ELK).
- Excellent problem‑solving skills and a collaborative mindset.
Skills
pythonmachine learningawskubernetesdocker