Job
Description
Responsibilities Manage Data: Extract, clean, and structure both structured and unstructured data. Coordinate Pipelines: Utilize tools such as Airflow, Step Functions, or Azure Data Factory to orchestrate data workflows. Deploy Models: Develop, fine-tune, and deploy models using platforms like SageMaker, Azure ML, or Vertex AI. Scale Solutions: Leverage Spark or Databricks to handle large-scale data processing tasks. Automate Processes: Implement automation using tools like Docker, Kubernetes, CI/CD pipelines, MLFlow, Seldon, and Kubeflow. Collaborate Effectively: Work alongside engineers, architects, and business stakeholders to address and resolve real-world problems efficiently. Qualifications 3+ years of hands-on experience in MLOps (4-5 years of overall software development experience). Extensive experience with at least one major cloud provider (AWS, Azure, or GCP). Proficiency in using Databricks, Spark, Python, SQL, TensorFlow, PyTorch, and Scikit-learn. Expertise in debugging Kubernetes and creating efficient Dockerfiles. Experience in prototyping with open-source tools and scaling solutions effectively. Strong analytical skills, humility, and a proactive approach to problem-solving. Preferred Qualifications Experience with SageMaker, Azure ML, or Vertex AI in a production environment. Commitment to writing clean code, creating clear documentation, and maintaining concise pull requests. Skills: sql,kubeflow,spark,docker,databricks,ml,gcp,mlflow,kubernetes,aws,pytorch,azure,ci/cd,tensorflow,scikit-learn,seldon,python,mlops Show more Show less
