Who We Are
Artmac Soft is a technology consulting and service-oriented IT company dedicated to providing innovative technology solutions and services to Customers.
Job Description
Job Title : MLOps Engineer
Job Type : C2C/1099
Experience: 9-20 Years
Location : Sunnyvale,California
We are looking for an MLOps Engineer who will be responsible for designing, building, and maintaining machine learning infrastructure and pipelines. This role requires a deep understanding of both software engineering and data science, with a focus on deploying and maintaining scalable machine learning solutions in production environments.
Responsibilities
- Having experience in product-based technologies Data Engineer, Infrastructure, Load Testing, Python, and DevOps.
- Experience with containerization and orchestration tools like Docker and Kubernetes.
- Hands-on experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
- Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.
- Strong understanding of CI/CD tools and practices (e.g., Jenkins, GitLab CI/CD).
- Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack).
- Design and develop robust data pipelines to support machine learning workflows.
- Ensure data quality, integrity, and consistency across various data sources.
- Optimize resource utilization and manage cloud resources (e.g., AWS, Azure, GCP).
- Implement and manage container orchestration using Kubernetes or similar technologies.
- Develop and execute load testing strategies to ensure the scalability and performance of machine learning systems.
- Identify bottlenecks and optimize system performance under high load conditions.
- Develop and maintain Python scripts and libraries for data processing, model training, and deployment.
- Implement CI/CD pipelines to automate the deployment of machine learning models.
- Manage configuration and deployment of infrastructure as code (IaC) using tools like Terraform or Ansible.
- Monitor and maintain the health of production systems, ensuring high availability and reliability.
- Maintain comprehensive documentation of ML pipelines, infrastructure, and processes.
Qualification
- Bachelor's degree or equivalent combination of education and experience