MLOps Engineer
Boston
Merlin Labs
Merlin is the aviation technology company propelling the future of fully autonomous flight.Responsibilities
- Design, build, and maintain scalable and reliable infrastructure for machine learning and software applications.
- Develop and implement CI/CD pipelines to automate the deployment of ML models and software applications.
- Monitor and manage the performance, availability, and security of ML models and applications in production.
- Collaborate with data scientists and software engineers to streamline the development and deployment process.
- Implement and manage containerization technologies (Docker, Kubernetes) to ensure efficient resource utilization.
- Automate infrastructure provisioning and configuration using tools like Terraform, Ansible, or similar.
- Ensure best practices for version control, testing, and documentation are followed.
- Troubleshoot and resolve issues related to infrastructure, deployment, and performance.
- Stay up-to-date with the latest industry trends and technologies in DevOps and ML Ops.
Qualifications:
- 3+ years of experience in DevOps, ML Ops, or a related role.
- 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.
- Solid understanding of software development lifecycle (SDLC) and Agile methodologies.
- Experience with monitoring and logging tools (Prometheus, Grafana, ELK stack).
- Strong scripting skills in Python, Bash, or similar languages.
- Excellent problem-solving skills and attention to detail.
- Strong communication and collaboration skills.
Nice to Have:
- Experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn).
- Knowledge of data pipeline tools (Apache Airflow, Luigi).
- Experience with model serving and monitoring tools (Kubeflow, MLflow, Seldon).
- Familiarity with security best practices in DevOps and ML Ops.
- 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
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Agile Airflow Ansible AWS Azure CI/CD CloudFormation DevOps Docker ELK Excel GCP GitHub GitLab Grafana Jenkins Kubeflow Kubernetes Machine Learning MLFlow ML models MLOps Model training Pipelines Python PyTorch Scikit-learn SDLC Security Seldon TensorFlow Terraform Testing
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