Qualifications
* 5+ years of experience in devops, cloud engineering, or ml engineering
* 3+ years of hands‐on experience in mlops or operationalizing ml models in production environments
key responsibilities
* architect and implement scalable end-to-end ml pipelines (training, validation, deployment, monitoring)
* design and maintain ci/cd pipelines for ml workflows using azure devops
* implement automated model versioning, artifact management, and rollback strategies
* provision and manage infrastructure using infrastructure as code (terraform, arm)
* deploy containerized ml services using docker and kubernetes
* implement monitoring frameworks for model performance, drift detection, and data quality
* optimize inference performance, scalability, and cost efficiency
* ensure compliance, governance, and security best practices in cloud ml environments
* provide technical leadership and mentorship to junior engineers
* collaborate closely with data science and engineering teams to define production standards
required skills
* strong experience with microsoft azure (required)
* experience with aws or gcp (plus)
* advanced knowledge of docker
* strong hands‐on experience with kubernetes (production clusters)
* advanced proficiency in python
* experience with bash and/or powershell
* experience designing and consuming rest apis
* experience with tensorflow, pytorch, or scikit-learn
* familiarity with ml lifecycle tools such as mlflow, kubeflow, dvc, or tfx
* experience with orchestration tools such as apache airflow or prefect
* implementation of model drift detection and performance monitoring frameworks
preferred certifications #j-18808-ljbffr