Sr. Python MLOps Engineer

Role Overview: You will be responsible for Python Development and MLOps Engineering, focusing on Modeling Automation, ML pipeline development, MLOps frameworks, and model lifecycle management. Your role will involve working on model training pipelines, artifact versioning, model registry management, CI/CD pipelines, Docker, Kubernetes, cloud-native deployments, scripting, automation, and debugging. Additionally, you will utilize Git, ML experiment tracking, and monitoring tools to enhance the development process. Key Responsibilities: - Possess 7+ years of experience in Python Development and MLOps Engineering - Demonstrate strong expertise in Modeling Automation and ML pipeline development - Utilize MLOps frameworks for model lifecycle management - Implement model training pipelines and workflow orchestration - Manage artifact versioning and model registry effectively - Utilize GCP Vertex AI or Azure ML for machine learning projects - Implement CI/CD pipelines for efficient ML deployments - Proficient in Docker, Kubernetes, and cloud-native deployments - Demonstrate strong scripting, automation, and debugging skills - Utilize Git, ML experiment tracking, and monitoring tools for efficient development Qualifications Required: - Experience with MLflow, Kubeflow, Airflow, or Prefect is a plus - Exposure to LLMOps / Generative AI deployment workflows is desirable - Knowledge of Terraform or Infrastructure as Code (IaC) is beneficial - Familiarity with Databricks or Spark-based ML workloads is advantageous - Experience with feature stores and data versioning is preferred - Exposure to AWS SageMaker is a bonus - Agile/Scrum development experience is beneficial - Strong understanding of scalable AI platform architecture is required Role Overview: You will be responsible for Python Development and MLOps Engineering, focusing on Modeling Automation, ML pipeline development, MLOps frameworks, and model lifecycle management. Your role will involve working on model training pipelines, artifact versioning, model registry management, CI/CD pipelines, Docker, Kubernetes, cloud-native deployments, scripting, automation, and debugging. Additionally, you will utilize Git, ML experiment tracking, and monitoring tools to enhance the development process. Key Responsibilities: - Possess 7+ years of experience in Python Development and MLOps Engineering - Demonstrate strong expertise in Modeling Automation and ML pipeline development - Utilize MLOps frameworks for model lifecycle management - Implement model training pipelines and workflow orchestration - Manage artifact versioning and model registry effectively - Utilize GCP Vertex AI or Azure ML for machine learning projects - Implement CI/CD pipelines for efficient ML deployments - Proficient in Docker, Kubernetes, and cloud-native deployments - Demonstrate strong scripting, automation, and debugging skills - Utilize Git, ML experiment tracking, and monitoring tools for efficient development Qualifications Required: - Experience with MLflow, Kubeflow, Airflow, or Prefect is a plus - Exposure to LLMOps / Generative AI deployment workflows is desirable - Knowledge of Terraform or Infrastructure as Code (IaC) is beneficial - Familiarity with Databricks or Spark-based ML workloads is advantageous - Experience with feature stores and data versioning is preferred - Exposure to AWS SageMaker is a bonus - Agile/Scrum development experience is beneficial - Strong understanding of scalable AI platform architecture is required