Machine Learning Engineer
Summary
Build and maintain scalable MLOps pipelines to deploy, monitor, and manage machine learning models in production using cloud platforms, CI/CD, and Kubernetes.
We are looking for an experienced MLOps Engineer (L3/L4) with 5-8 years of experience to build, deploy, monitor, and manage machine learning solutions in production. The ideal candidate should have strong expertise in cloud platforms, CI/CD, containerization, automation, and machine learning lifecycle management.
Key Responsibilities
- Design, build, and maintain scalable MLOps pipelines.
- Deploy and manage machine learning models in production.
- Develop CI/CD pipelines for ML workflows.
- Automate model training, testing, deployment, and monitoring.
- Work closely with Data Scientists, Data Engineers, and Software Engineers.
- Monitor model performance and ensure reliability and scalability.
- Manage infrastructure using Infrastructure as Code (IaC).
- Implement security, governance, and best practices for ML platforms.
- Troubleshoot production issues and optimize ML systems.
Required Skills
- 5-8 years of experience in MLOps, DevOps, or Machine Learning Engineering.
- Strong experience with Azure Machine Learning, Azure DevOps, or Databricks.
- Good knowledge of Python and SQL.
- Experience with Docker and Kubernetes.
- Hands-on experience with CI/CD tools such as Azure DevOps, GitHub Actions, or Jenkins.
- Knowledge of ML lifecycle management and model deployment.
- Experience with Git version control.
- Familiarity with Terraform or other Infrastructure as Code tools.
- Understanding of monitoring tools such as Prometheus, Grafana, or Azure Monitor.
Preferred Skills
- Experience with MLflow.
- Knowledge of Apache Airflow or similar workflow orchestration tools.
- Experience with Spark and Databricks.
- Knowledge of REST APIs and microservices.
- Understanding of cloud security and governance.
- Experience with Generative AI or Large Language Model (LLM) deployment is an added advantage.
Qualifications
- Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, or a related field.
- Relevant Azure, Kubernetes, or Databricks certifications are preferred.
Good to Have
- Strong communication and problem-solving skills.
- Experience working in Agile/Scrum environments.
- Ability to collaborate with cross-functional teams.
- Experience supporting enterprise-scale machine learning platforms.