AI MLOps Engineer
March 21
Description
• Build complex automated reproducible pipelines for the entire ML project lifecycle, including data management, experimentation, model training, deployment and monitoring in production • Build reusable integrations and applications for each stage of the ML project lifecycle • Monitor the MLOps tools/approaches landscape to actively identify solutions for various domain problems • Participate in the development of MLOps platform services • Communicate with end users to understand their pain points • On-board end users to the MLOps platform
Requirements
• Understanding of ML-driven projects lifecycle • Knowledge and hands-on experience with Kubernetes, Containerd / Docker, Helm, CI/CD practices, particularly with GitHub Actions • Basic understanding of MLOps best practices (data and model versioning, experiment tracking, reproducibility, etc.) • Proficient in Python for scripting, automation and integration • Networking including TCP/IP, DNS, load balancing and requests routing to ensure secure and efficient network operations • Experience with Pachyderm, DVC, KubeFlow, Spark, MLFlow, Seldon or their alternatives • Experience with LLM deployment at scale
Benefits
• Work remotely, ensuring time zones align for effective collaboration. • Shape the product's direction and success by taking ownership of essential components. • Solve complex and innovative challenges. • Join a supportive and dynamic team environment. • Receive a competitive salary and benefits package.
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