We are seeking a highly skilled and motivated MLOps Engineer with 3-5 years of experience to join our engineering team. The ideal candidate should possess a strong foundation in DevOps or software engineering principles with practical exposure to machine learning operational workflows. You will be instrumental in operationalizing ML systems, optimizing the deployment lifecycle, and strengthening the integration between data science and engineering teams.
Required Skills:
• Hands-on experience with MLOps platforms such as MLflow and Kubeflow.
• Proficiency in Infrastructure as Code (laC) tools like Terraform or Ansible.
• Strong familiarity with monitoring and alerting frameworks (Prometheus, Grafana, Datadog, AWS CloudWatch).
• Solid understanding of microservices architecture, service discovery, and load balancing.
• Excellent programming skills in Python, with experience in writing modular, testable, and maintainable code.
• Proficient in Docker and container-based application deployments.
• Experience with CI/CD tools such as Jenkins or GitLab Cl.
• Basic working knowledge of Kubernetes for container orchestration.
• Practical experience with cloud-based ML platforms such as AWS SageMaker, Databricks, or Google Vertex Al.
Good-to-Have Skills:
• Awareness of security practices specific to ML pipelines, including secure model endpoints and data protection.
• Experience with scripting languages like Bash or PowerShell for automation tasks.
• Exposure to database scripting and data integration pipelines.
Experience & Qualifications:
• 3-5+ years of experience in MLOps, Site Reliability Engineering (SRE), or
Software Engineering roles.
• At least 2+ years of hands-on experience working on ML/Al systems in production settings.