Darum lohnt es sich
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. #J-18808-Ljbffr
