Alvyl Consulting
Website:
alvyl.com
Job details:
Key Skills
MLOps, MLFlow, Python, Docker, Jenkins, Kubernetes, Automation, CI/CD, SDLC, Deployment, Machine Learning
Key Responsibilities
- Design and develop MLOps pipelines for deployment and integration of ML models.
- Collaborate with data scientists and engineering teams to operationalize machine learning models.
- Automate model training, testing, and deployment workflows using Python and Shell scripting.
- Monitor models in production and identify performance issues or anomalies.
- Implement and maintain version control practices for ML models and datasets.
- Containerize ML services and applications using Docker.
- Build and maintain CI/CD pipelines using Jenkins or similar tools.
- Support Kubernetes-based deployment and orchestration of ML workloads.
- Ensure adherence to security, data privacy, and governance standards.
- Document MLOps workflows, configurations, and best practices.
- Stay updated with emerging MLOps tools, technologies, and industry trends.
- Participate in Agile ceremonies including sprint planning, standups, and retrospectives.
Required Skills & Experience
- 2–4 years of experience in MLOps, DevOps, or software engineering with exposure to Machine Learning.
- Hands-on experience or strong familiarity with MLFlow.
- Strong proficiency in Python and Shell/Bash scripting.
- Experience with Docker for containerization.
- Familiarity with Jenkins or other CI/CD tools.
- Basic understanding of Kubernetes and container orchestration.
- Understanding of machine learning concepts and the ML lifecycle.
- Familiarity with SDLC practices and Agile methodologies.
- Strong analytical and problem-solving abilities.
- Good communication and collaboration skills.
- Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field.
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