ML Ops engineerTrack This Job
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Title: Sr. ML Ops engineer
Location: 100% remote
Duration: 12 months contract
Must have: MLOps engineering, Databricks, monitoring and observability stacks (e.g., Prometheus, Grafana, ELK, or Datadog). Strong programming skills in Python and familiarity with containerization technologies like Docker and Kubernetes. Excellent problem-solving and communication skills.
Required Skills and Experience:
- Proven experience in MLOps engineering, with a strong understanding of ML lifecycle management
- Expertise in Databricks and cloud-native ML environments (e.g., Azure ML, AWS SageMaker, GCP Vertex AI)
- Proficiency in tools such as MLflow, Kubeflow, Airflow, or similar orchestration frameworks
- Experience with monitoring and observability stacks (e.g., Prometheus, Grafana, ELK, or Datadog)
- Strong programming skills in Python and familiarity with containerization technologies like Docker and Kubernetes
- Excellent problem-solving and communication skills
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