MLOps Engineer

💼 Level: Mid-Level

We’re looking for a skilled and automation-focused MLOps Engineer to join our technology and AI team and build reliable, scalable, and efficient machine learning infrastructure and deployment environments. In this role, you will bridge the gap between machine learning development and production operations by designing automated ML pipelines, improving model deployment workflows, and establishing robust monitoring and lifecycle management practices. You'll collaborate closely with machine learning engineers, data scientists, software engineers, DevOps engineers, cloud architects, and business stakeholders to deliver secure, scalable, and high-performing machine learning solutions. If you're passionate about machine learning infrastructure, automation, cloud technologies, and production AI systems, we'd love to hear from you!

🎯 Key Responsibilities

• Design, build, and maintain scalable MLOps infrastructure and machine learning production environments.
• Develop and optimize automated machine learning pipelines covering data preparation, model training, validation, deployment, and monitoring.
• Implement CI/CD and continuous training (CT) workflows to automate machine learning model development and deployment processes.
• Build and maintain model deployment platforms, model registries, feature stores, and experiment tracking systems.
• Collaborate with data scientists and machine learning engineers to productionize models and improve model lifecycle management.
• Implement infrastructure-as-code (IaC) solutions using tools such as Terraform, CloudFormation, or similar technologies.
• Manage cloud-based machine learning infrastructure across platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build and manage containerized ML environments using Docker, Kubernetes, and cloud-native technologies.
• Implement monitoring and observability solutions for model performance, data quality, model drift, system reliability, and infrastructure health.
• Automate model versioning, testing, deployment, rollback, and lifecycle management processes.
• Optimize machine learning infrastructure for scalability, performance, reliability, security, and cost efficiency.
• Troubleshoot production ML systems, deployment failures, pipeline issues, and infrastructure problems.
• Collaborate with DevOps and software engineering teams to integrate MLOps practices into broader engineering workflows.
• Implement security, access controls, data protection, and responsible AI practices across machine learning environments.
• Maintain technical documentation, architecture diagrams, operational procedures, and MLOps standards.
• Research emerging MLOps technologies, machine learning platforms, cloud services, automation tools, and industry best practices.
• Contribute to continuous improvement initiatives that enhance machine learning delivery, system reliability, engineering efficiency, and business outcomes.

✅ Requirements

• Strong experience in MLOps, machine learning engineering, DevOps, cloud engineering, software engineering, or a related technical field.
• Strong proficiency in Python and experience with scripting, automation, and software development practices.
• Solid understanding of machine learning workflows, model lifecycle management, and production ML systems.
• Experience building CI/CD pipelines and automated deployment workflows using tools such as GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar platforms.
• Experience with cloud platforms such as AWS, Microsoft Azure, Google Cloud, or similar technologies.
• Strong knowledge of Docker, Kubernetes, container orchestration, and cloud-native architectures.
• Experience with infrastructure-as-code tools such as Terraform, Ansible, CloudFormation, or similar technologies.
• Familiarity with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, Amazon SageMaker, Azure Machine Learning, or similar solutions.
• Understanding of model monitoring, data drift, model drift, performance tracking, observability, and ML system reliability.
• Experience with databases, data pipelines, APIs, version control, and software development lifecycle practices.
• Knowledge of Linux systems, networking, security, identity and access management, and cloud infrastructure.
• Familiarity with feature stores, model registries, experiment tracking, automated model validation, and continuous training is advantageous.
• Strong analytical thinking, troubleshooting, debugging, and problem-solving skills.
• Excellent communication and collaboration abilities with data scientists, engineers, and technical stakeholders.
• High level of professionalism, accountability, adaptability, and commitment to continuous learning.
• Bachelor's or master's degree in Computer Science, Machine Learning, Data Science, Software Engineering, Information Technology, or a related field is preferred.
• Relevant certifications in cloud computing, DevOps, machine learning, or MLOps are advantageous.

🌟 What We Offer

• An exciting opportunity to build modern MLOps platforms and production machine learning infrastructure.
• Exposure to cutting-edge technologies including machine learning, generative AI, cloud computing, Kubernetes, automation, and AI-powered platforms.
• Opportunities to work closely with data scientists, machine learning engineers, software developers, DevOps engineers, and cloud architects.
• Career growth and professional development opportunities in a rapidly evolving technology field.
• Ongoing technical training, mentorship, and certification support.
• Access to modern cloud platforms, ML infrastructure, DevOps tools, automation technologies, and emerging AI solutions.
• Competitive salary and comprehensive benefits package.
• A collaborative engineering culture that values automation, innovation, reliability, technical excellence, and continuous improvement.
• The opportunity to make a meaningful impact by improving machine learning deployment, accelerating AI innovation, strengthening system reliability, and supporting long-term digital transformation.