MLOps Engineer

Job Description

Key Responsibilities Design, deploy, and maintain robust machine learning infrastructure. Partner with Data Scientists and ML Engineers to manage ML model deployment and lifecycle. Develop and maintain CI/CD pipelines for ML workflows. Monitor, optimize, and troubleshoot production ML systems and infrastructure. Implement automated validation and testing frameworks for ML models. Ensure security, compliance, and versioning of ML systems and datasets. Document workflows, best practices, and infrastructure design. Stay current with advancements in MLOps and cloud-native tools. Must-Have Qualifications Bachelor’s degree in Computer Science or Engineering (no B.Com/B.Sc). 2–5 years of experience in an SRE or related infrastructure-focused role. Strong foundation in Machine Learning concepts and workflows. Proficiency in at least one major programming language: Python, Java, or Go. Hands-on experience with Cloud platforms: AWS, Azure, or GCP. Proficiency with Docker and Kubernetes for containerized environments. Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI. Excellent troubleshooting, collaboration, and communication skills. No frequent job changes or employment gaps. ⚠️ Note : Candidates with only DevOps experience and no ML exposure will not be considered. Preferred Qualifications Master’s degree in Computer Science or related discipline. Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn. Familiarity with tools like Apache Spark, Kafka, Airflow for data workflows. Exposure to monitoring tools such as Prometheus, Grafana, ELK stack. Knowledge of Infrastructure as Code (IaC): Terraform, Ansible. Experience with automated model testing and ML pipeline validation. Understanding of security best practices for AI/ML systems. Skills: gcp,design,aws,apache spark,airflow,learning,infrastructure,ci,terraform,kubernetes,pytorch,tensorflow,ci/cd tools (jenkins, gitlab ci, circleci),ml,prometheus,ansible,go,java,cd,elk stack,scikit-learn,computer science,azure,kafka,python,grafana,machine learning,machine learning concepts,docker,cloud Show more Show less