Python Software Engineer – Machine Learning Systems

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

• 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 (Git Hub Actions, Git Lab 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)

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.