Ml Ops & Observability Engineer

pfizer.ch

Hybrid
Mlops platform design and implementation
Ci/cd pipelines for ml workloads
Cloud-native ml environments (aws/azure)
You will play a pivotal role in implementing impactful and innovative technology solutions across all functions, from research to manufacturing

Job Summary

  • You will play a pivotal role in implementing impactful and innovative technology solutions across all functions, from research to manufacturing.
  • Lead the design, implementation, and operation of MLOps platforms supporting model development, deployment, monitoring, and lifecycle management.
  • Own operational reliability for ML platforms and services and lead response to ML-related production incidents.

Matching Summary

You will play a pivotal role in implementing impactful and innovative technology solutions across all functions, from research to manufacturing.

Skills & Requirements

Must-have

  • MLOps platform design and implementation
  • CI/CD pipelines for ML workloads
  • Cloud-native ML environments (AWS/Azure)
  • End-to-end observability for ML systems
  • OpenTelemetry, Prometheus, Grafana, ELK
  • ML lifecycle testing and validation
  • Responsible AI operations support
  • Operational reliability for ML platforms
  • Incident response for ML production incidents
  • Python, Bash, SQL proficiency

Nice-to-have

  • SRE-inspired practices
  • Coaching engineers on MLOps best practices
  • Strong engineering discipline
  • Experience with MLOps platforms (MLflow, Kubeflow)
  • Data drift detection and model monitoring
  • Familiarity with regulated environments

Key Requirements

  • 8+ years of experience in ML engineering, MLOps, platform engineering
  • 3+ years of people leadership
  • Hands-on experience operationalizing ML systems in AWS or Azure
  • Experience with MLOps pipelines and tooling
  • Experience with CI/CD for ML workloads
  • Experience with containerized and cloud-native ML runtimes
  • Solid understanding of testing and validation for ML systems
  • Strong experience implementing observability and reliability practices
  • Demonstrated experience with DevSecOps and secure SDLC for AI/ML systems

Work Rights

Not specified

Tailored Resume

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