Machine Learning Operations Engineer
Team: Autonomy Software
Location: Boston
Commitment: Full Time
Workplace Type: hybrid
Responsibilities:
- Design, build, and maintain scalable and reliable infrastructure for machine learning, data management and software applications.
- Develop, implement and maintain CI/CD pipelines to automate the deployment and testing of ML models.
- Monitor and manage the performance, availability, and security of ML models and applications in production.
- Implement and manage containerization technologies (Docker, Kubernetes) to ensure efficient resource utilization.
- Automate infrastructure provisioning and configuration using tools like Terraform, Ansible, or similar.
- Manage and maintain ML team’s database
- Ensure best practices for version control, testing, and documentation are followed.
- Stay up-to-date with the latest industry trends and technologies in DevOps and MLOps.
Qualifications:
- 3+ years of experience in DevOps, MLOps, or a related role.
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Strong knowledge of cloud platforms (AWS, Azure, GCP) and cloud-native services.
- Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins, or similar tools.
- Proficiency in containerization technologies (Docker, Kubernetes).
- Familiarity with infrastructure as code (IaC) tools like Terraform, Ansible, or CloudFormation.
- Database Architecture or Management Experience
- Solid understanding of software development lifecycle (SDLC) and Agile methodologies.
- Strong scripting skills in Python, Bash, or similar languages.
- Excellent problem-solving skills and attention to detail.
- Strong communication and collaboration skills.
Nice to haves:
- Experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn).
- Experience with MLOps platforms such as Weights and Biases or MLflow
- Familiarity with security best practices in DevOps and MLOps.
- Experience working on software development processes for a regulated environment (aerospace, medical, automotive, etc.)
- Experience with building scalable machine learning model training infrastructure in the cloud
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