Job Description

As an ML Ops / Data Operations Engineer, you will be responsible for building and managing robust data pipelines and ML workflows. You will work closely with data scientists, machine learning engineers, and infrastructure teams to streamline the training and deployment of machine learning models. Your role ensures that data is accessible, versioned, and high-quality throughout the ML lifecycle.

• Develop and manage scalable data and ML pipelines.
• Collaborate with cross-functional teams to automate data workflows.
• Ensure reproducibility and versioning of datasets and ML models.
• Monitor, troubleshoot, and optimize ML infrastructure and jobs.

Responsibilities

• Build and maintain automated data ingestion and transformation pipelines.
• Design systems for dataset versioning and tracking data lineage.
• Integrate MLOps tools to manage model training, testing, and deployment workflows.
• Monitor and optimize machine learning workflows and infrastructure.
• Collaborate with ML engineers to scale model experimentation and training processes.
• Implement and enforce best practices for data quality, governance, and reproducibility.

Skills & Qualifications

• 3+ years of experience in ML Ops, Data Engineering, or related roles.
• Proficiency with tools such as Airflow, MLflow, DVC, Docker, and Kubernetes.
• Experience with Python, SQL, and version control systems like Git.
• Familiarity with cloud platforms like AWS, GCP, or Azure.
• Strong understanding of machine learning pipelines and data lifecycle management.
• Excellent problem-solving and communication skills.

Benefits

• Competitive salary and performance bonuses.
• Remote-friendly work environment.
• Opportunities to work with cutting-edge AI/ML systems.
• Learning budget and access to online courses and certifications.
• Collaborative, forward-thinking team culture.