Job
Description
What You’ll Do Handle data: pull, clean, and shape structured & unstructured data. Manage pipelines: Airflow / Step Functions / ADF… your call. Deploy models: build, tune, and push to production on SageMaker, Azure ML, or Vertex AI. Scale: Spark / Databricks for the heavy lifting. Automate processes: Docker, Kubernetes, CI/CD, MLFlow, Seldon, Kubeflow. Collaborate effectively: work with engineers, architects, and business professionals to solve real problems promptly. What You Bring 3+ years hands-on MLOps (4-5 yrs total software experience). Proven experience with one hyperscaler (AWS, Azure, or GCP). Confidence with Databricks / Spark, Python, SQL, TensorFlow / PyTorch / Scikit-learn. Extensive experience handling and troubleshooting Kubernetes and proficiency in Dockerfile management. Prototyping with open-source tools, selecting the appropriate solution, and ensuring scalability. Analytical thinker, team player, with a proactive attitude. Nice-to-Haves Sagemaker, Azure ML, or Vertex AI in production. Dedication to clean code, thorough documentation, and precise pull requests. Skills: mlflow,ml ops,scikit-learn,airflow,mlops,sql,pytorch,adf,step functions,kubernetes,gcp,kubeflow,python,databricks,tensorflow,aws,azure,docker,seldon,spark Show more Show less
