Machine Learning OP's Engineer - Lead (Hybrid)

Responsibilities

• Designing and implementing robust MLOps pipelines to streamline the ML lifecycle, from data ingestion to model deployment and monitoring.
• Developing and maintaining CI/CD pipelines for ML models, ensuring efficient and reliable deployment.
• Building and managing ML infrastructure on cloud platforms, with a focus on Amazon SageMaker.
• Optimizing model performance and resource utilization in production environments.
• Monitoring model performance and finding opportunities for improvement.
• Collaborating with data scientists and engineers to improve ML model development processes.
• Ensuring data quality and integrity throughout the ML pipeline.

Requirements

• A minimum of 7 Years of experience Ops Engineering and 4 years of Machine Learning experience.
• Strong proficiency in Python programming language.
• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Expertise in cloud platforms, particularly Amazon Web Services (AWS) and Amazon SageMaker.
• In-depth knowledge of MLOps tools and technologies (e.g., Docker, Kubernetes, Jenkins, Airflow).
• Experience with version control systems (Git).
• Understanding of data engineering concepts and tools (e.g., SQL, ETL pipelines).
• Proficiency in cloud-based data storage and processing services (e.g., S3, EMR, Redshift).
• Knowledge of big data technologies is a plus.
• Have knowledge about data engineering concepts, tools and automation processes (DataOps) since data pipelines and architectures provide the base for building AI solutions.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.
• Ability to work independently and as part of a team.
• Attention to detail and focus on quality.
• Passion for machine learning and data science.
• A continuous learner with a desire to stay updated on the latest industry trends.