Machine Learning Engineer

SMARTFOX is seeking an MLOps Engineer to deploy, automate, and maintain machine learning models in production environments. The role involves building scalable ML pipelines, collaborating with teams to operationalize solutions, and ensuring model performance and reliability.

Responsibilities:

• Build, deploy, and maintain scalable machine learning pipelines for model training and inference
• Automate ML workflows using CI/CD tools and infrastructure-as-code practices
• Deploy ML models using Docker, Kubernetes, and cloud-based services
• Collaborate with Data Scientists and Software Engineers to operationalize machine learning solutions
• Monitor model performance, data drift, and system reliability in production environments
• Implement model versioning, experiment tracking, and reproducible ML workflows
• Optimize infrastructure for model serving, scalability, and cost efficiency
• Troubleshoot deployment, performance, and infrastructure-related issues
• Maintain documentation and follow MLOps best practices for security and compliance

Requirements:

• Bachelor's degree in computer science, Data Science, Information Technology, or a related field
• 2–3 years of experience in MLOps, Machine Learning Engineering, or DevOps supporting ML workloads
• Strong programming skills in Python
• Experience with Docker, Kubernetes, Git, and Linux
• Knowledge of AWS, Azure, or Google Cloud Platform services for machine learning
• Experience with MLflow, Kubeflow, Airflow, or Apache Spark
• Familiarity with TensorFlow, PyTorch, or Scikit-learn
• Experience building CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps
• Understanding of REST APIs, model serving, monitoring, and logging tools
• Strong knowledge of SQL and version control using Git
• Experience with Terraform or Ansible for infrastructure automation
• Familiarity with feature stores, model registries, and experiment tracking
• Knowledge of monitoring tools such as Prometheus, Grafana, or ELK Stack
• Exposure to serverless ML deployments and GPU-based workloads
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• Certified Kubernetes Application Developer (CKAD)