MLOps Engineer

• Collaborate with data scientists and data engineers to operationalize models
• Monitor model performance, drift, and data quality in production
• Automate model retraining and deployment processes
• Manage cloud-based ML infrastructure and services
• Ensure scalability, reliability, and security of ML systems

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field
• 2–5 years of experience in MLOps, Data Engineering, or Machine Learning Engineering
• Strong programming skills in Python
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Hands-on experience with cloud platforms (AWS, Azure, or GCP)
• Knowledge of Docker and container orchestration (Kubernetes preferred)
• Experience with CI/CD tools and automation workflows
• Understanding of ML model lifecycle and deployment strategies

Preferred Qualifications

• Experience with ML platforms (SageMaker, Vertex AI, Azure ML)
• Familiarity with tools like MLflow, Kubeflow, Airflow, or DVC
• Knowledge of streaming and big data tools (Kafka, Spark)
• Experience with model monitoring and observability tools
• Understanding of infrastructure-as-code (Terraform, CloudFormation)

Soft Skills

• Strong analytical and problem-solving abilities
• Good communication and collaboration skills
• Ability to work with cross-functional teams (Data Science, Engineering, Product)
• Attention to detail and system reliability mindset
• Ability to work in fast-paced environments