MLOps Engineer
1 week ago
What will your role look like
As an MLOps Engineer, you will play a key role in deploying, monitoring, and maintaining machine learning models in production environments. Youll be responsible for designing scalable ML pipelines, optimizing performance, and ensuring model reproducibility using modern MLOps tools and frameworks.
You will also drive DevOps-related activitiesdeploying product updates, identifying production issues, implementing integrations, and managing CI/CD pipelines. Working closely with developers, data scientists, and IT teams, youll ensure smooth code releases, reliable infrastructure, and top-tier system performance.
Why you will love this role
- 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.
We would like you to bring along
- 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 or JavaScript for automation or backend integrations
- Passion for building reliable, scalable, and efficient systems
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