Responsibilities
- Design, build, and maintain production ML systems and pipelines
- Write clean, testable, and maintainable Python code
- Deploy and operate models in production (APIs, batch jobs, real-time systems)
- Work hands‑on with AWS infrastructure to build scalable systems
- Use distributed systems (Ray) for large‑scale workloads and model serving
- Containerize and deploy services using Docker (Kubernetes is a plus)
- Improve and maintain CI/CD pipelines for ML workflows
- Ensure robust testing, monitoring, and reliability of ML systems
- Contribute to improving system architecture, performance, scalability and cost efficiency
- Treat modeling as part of software engineering—not a separate activity
Requirements
- 4+ years of experience in software engineering
- Hands‑on experience building, deploying, and operating ML systems in production
- Strong programming skills in Python (clean architecture, testing, modular design not just scripts)
- Proven experience building and operating systems on AWS (preferred) or strong experience with a comparable cloud platform
- Experience with Ray (or similar distributed compute frameworks) is a strong plus.
- Experience with: Git (collaborative workflows, code reviews)
- CI/CD pipelines (GitHub Actions, GitLab CI, etc.)
- Testing (unit + integration — not optional)
- Experience with Docker (Kubernetes or similar is a strong plus)
- Hands‑on experience with production infrastructure (CI/CD, monitoring, logging, deployments)
Core Competencies
Demonstrates expertise in designing, building, and maintaining production ML systems and pipelines, with a strong focus on Python programming, AWS infrastructure, and CI/CD workflows. Proven ability to ensure system reliability and performance through robust testing and monitoring practices.
