MLOps Engineer @ Infobeans

• Youll work at the intersection of Machine Learning and DevOps, bridging model development with real-world deployment.
• Youll be part of a customer-focused team that values innovation, automation, and reliability.
• Youll get to experiment with cutting-edge tools like Kubeflow, Airflow, MLflow, TensorFlow Serving, and Prometheus.
• Youll directly contribute to improving customer experience by ensuring high-performance and resilient systems.
• Youll collaborate with passionate engineers and data scientists who love solving complex problems and building scalable solutions.

• Education: Bachelors degree in Computer Science, Information Technology, or a related field.
• Experience of 35 years in MLOps, DevOps, or software development (with focus on automation and scalability).
• Strong understanding of containerization (Docker, Kubernetes)
• Experience with CI/CD tools Jenkins, GitLab CI, Ansible, Chef, Puppet, Terraform
• Expertise in cloud platforms AWS, Azure, or GCP
• Familiarity with ML pipeline tools Kubeflow, Airflow, MLflow, Feast
• Knowledge of monitoring and logging tools Prometheus, Grafana, ELK Stack
• Proficiency in scripting languages Python, Bash, or Ruby
• Excellent problem-solving and analytical thinking
• Strong collaboration and communication abilities
• Ability to manage multiple projects and priorities effectively
• Detail-oriented with a proactive approach to automation and reliability
• Design and deploy scalable ML pipelines using Kubeflow and Airflow.
• Implement DevOps practices to streamline the software development lifecycle.
• Deploy and monitor ML models using TensorFlow Serving, Prometheus, and Grafana.
• Integrate and manage model versioning and experiment tracking with MLflow.
• Collaborate with data scientists to optimize feature engineering workflows (Feast).
• Develop and maintain reliable CI/CD pipelines for code and model deployment.
• Implement infrastructure monitoring, logging, and backup solutions.
• Perform root cause analysis and troubleshooting for production issues.
• Ensure security, scalability, and performance of deployed systems.
• Stay updated on emerging MLOps and DevOps tools and trends.

Good-to-have skills

• Experience with experiment tracking tools (Weights & Biases, MLflow)
• Exposure to Infrastructure as Code (IaC) tools like AWS CloudFormation or Ansible
• Understanding of blue-green and canary deployments
• Familiarity with Agile and DevOps methodologies
• Working knowledge of Node.js or JavaScript for automation or backend integrations
• Passion for building reliable, scalable, and efficient systems