MLOps Engineer

Summary

Join our team as an experienced MLOps Engineer to design, develop, and deploy machine learning models and pipelines. The ideal candidate will have a strong background in software engineering, data science, and DevOps practices.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field
• 9+ years of experience in software engineering, data engineering, or MLOps roles
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes)
• Strong experience with services like S3, Lambda, BigQuery, etc
• Hands-on experience with ETL/ELT tools and frameworks like Apache NiFi, Airflow, dbt, or similar
• Experience with data storage solutions and data modelling
• Experience with Medallion architecture scenarios
• Experience with ML tools and frameworks such as TensorFlow, PyTorch, Scikit-Learn, etc
• Proficiency in programming languages such as Python, with experience in developing and maintaining codebases
• Familiarity with infrastructure as code (IaC) tools like Terraform or CloudFormation

Responsibilities

• Collaborate with data scientists to deploy, scale, and manage machine learning models in production environments
• Design and implement automated ML pipelines using tools like Kubeflow, MLflow, Airflow, or similar to streamline the ML lifecycle
• Manage and optimize data platforms, databases, and data lakes (e.g., Hadoop, Spark, Redshift, Snowflake) to ensure high availability and performance
• Develop and maintain CI/CD pipelines for continuous integration, testing, and deployment of ML models and data pipelines
• Implement robust monitoring and logging to ensure model performance, detect anomalies, and manage model drift
• Implement monitoring solutions to track data quality, pipeline performance, and infrastructure health, and set up automated alerting and incident management
• Build and manage scalable infrastructure for ML experiments, including data storage, compute resources, and model serving
• Work closely with data scientists, data engineers, and DevOps teams to establish and enforce best practices for MLOps and ensure compliance with organizational policies
• Use tools like DVC, MLflow, or TensorBoard to track model versions, experiments, results and data
• Ensure that ML solutions and Data Solutions comply with data security, privacy regulations, and best practices
• Identify bottlenecks in data pipelines and infrastructure, and work on optimizing them for performance and cost-efficiency

Benefits

Work from home