ML Pipeline Engineer

We are looking for an ML Pipeline Engineer to take ownership of and enhance our continuous integration (CI) pipeline for machine learning. Our pipeline currently automates model retraining using data from MySQL, Snowflake, and BLOB storage, performs model performance checks, and ensures the integrity of our models. Now, we’re looking to extend its capabilities by improving model deployment and further optimizing automation.
This role requires a deep understanding of machine learning workflows, infrastructure, and automation.
RESPONSIBILITIES

• Build and maintain robust, scalable pipelines for data preprocessing, training, and inference.
• Automate machine learning workflows, including feature extraction, model training, and evaluation.
• Deploy machine learning models into production environments, ensuring high availability and performance.
• Monitor production systems for model drift, performance issues, and data quality.
• Collaborate with Data Scientists to integrate models into production environments effectively.
• Optimize infrastructure for cost, performance, and scalability, using cloud platforms and distributed systems.
• Implement CI/CD pipelines for machine learning workflows to enable faster and more reliable deployments.
• Ensure compliance with data governance, security, and regulatory standards in production systems.

QUALIFICATIONS

• Bachelor’s degree in Computer Science, Data Engineering, or a related field, or equivalent experience.
• 3+ years of experience in machine learning engineering, software engineering, and DevOps.
• Strong programming skills in Python and experience with ML libraries.
• Proficiency in cloud platforms and their ML services.
• Hands-on experience with containerization (Docker) and orchestration tools (Kubernetes) is a plus.
• Knowledge of distributed data processing frameworks (e.g., Apache Spark, Hadoop).
• Strong understanding of CI/CD practices and tools for ML pipelines.

PREFERRED QUALIFICATIONS

• Experience with monitoring tools for tracking model performance and system health.
• Familiarity with workflow orchestration tools.
• Knowledge of MLOps tools.
• Exposure to ML model training and deployment—and a strong desire to get hands-on with data science tasks.

TOOLS AND TECHNOLOGY

• Programming: Python, Bash scripting.
• ML Frameworks: PyTorch, Scikit-learn, XGBoost.
• Infrastructure: Docker, Kubernetes, Terraform.
• Cloud Platforms: Azure ML, Azure Function Apps

SOFT SKILLS

• Strong attention to detail and focus on reliability.
• Effective collaboration with cross-functional teams, including Data Scientists and DevOps.
• Problem-solving mindset with the ability to debug complex issues.
• Excellent organizational skills to manage and prioritize tasks effectively.