Senior MLOps Engineer for Scalable Machine Learning Infrastructure
3 weeks ago
About the Role
We are seeking a Senior MLOps Engineer to design, implement, and maintain scalable machine learning infrastructure. As a key member of our team, you will work closely with data scientists, software engineers, and IT teams to ensure seamless integration and deployment of ML models into production environments.
Key Responsibilities
- Design and implement scalable ML infrastructure using Kubernetes orchestration
- Collaborate with data scientists to understand model requirements and provide technical guidance on model optimization and deployment
- Develop, test, and deploy machine learning models using appropriate frameworks and libraries
- Research industry-leading machine learning platform technologies and create quick prototypes/proof-of-concepts
- Optimize resource utilization and ensure high availability and reliability of ML services on Kubernetes
- Provide technical support and training to team members on ML and DevOps practices
Requirements
- 6+ years of experience in machine learning engineering with 1⁄2 of the time being within DevOps environment
- Proven experience in deploying and managing ML models in production environments using Kubernetes
- Strong programming skills in Python, with experience in ML libraries such as TensorFlow, PyTorch, or scikit-learn
- Experience with MLOps tools such as Kubeflow, MLflow, or TFX
- Proficient in DevOps tools and practices including Docker, Jenkins, and Git
- Extensive experience with Kubernetes for container orchestration and management
- Hands-on experience with building ADF pipelines on Azure Databricks
Nice to Have Skills & Experience
- Knowledge of IBP (Integrated Business Planning)
- Familiarity with monitoring tools such as Prometheus and Grafana
- Knowledge of infrastructure-as-code tools like Terraform or Ansible
- Understanding of data engineering concepts and tools
Location
This role is remote in India; however, we are looking for someone who can work between 12:00 PM IST to 9:00 PM IST.
Compensation
This role targets candidates at a calculated pay range between $17-$20 USD per hour.
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