Senior DevOps Engineer - MLOps Focus

December 3, 2024

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Workforce Source

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Workforce Source is a company specializing in Recruiting Process Outsourcing (RPO) since 2003. They focus on providing results-oriented recruiting solutions to find top talent for their clients. Workforce Source maintains a vast proprietary database with millions of candidates and utilizes passive sourcing techniques to constantly add new candidates. They assist businesses of all sizes in enhancing their recruiting operations by offering innovative sourcing techniques, 24/7 recruiter support, and access to premium subscription services, aiming to extract maximum value and efficiency from their clients' recruiting processes.

Recruiting • Recruiting Process Outsourcing • Candidate Sourcing • Candidate Pipelining • Internet Research

11 - 50 employees

🎯 Recruitment

👥 HR Tech

🤝 B2B

📋 Description

• Design, implement, and maintain scalable CI/CD pipelines tailored for AI/ML projects. • Automate the deployment and monitoring of ML models in production using tools like Kubernetes, Docker, or similar containerization platforms. • Develop workflows for continuous model training, validation, and deployment. • Collaborate with Data Scientists to ensure seamless integration of models into production environments. • Implement versioning and tracking systems for datasets, models, and training pipelines (e.g., MLflow, DVC). • Build systems to monitor the performance of ML models in production and trigger retraining when necessary. • Optimize resource utilization for training and inference workloads on cloud or hybrid environments. • Partner with cross-functional teams, including Data Scientists, Engineers, and Product Managers, to align infrastructure goals with business needs. • Document processes and educate team members on best practices for DevOps and MLOps.

🎯 Requirements

• Proficiency in Python, Bash, or similar scripting languages. • Experience with CI/CD tools (e.g., Jenkins, GitLab CI/CD, CircleCI). • Strong understanding of containerization and orchestration tools (Docker, Kubernetes). • Familiarity with cloud platforms (AWS, Azure, GCP) and related services for ML workloads. • Hands-on experience with MLOps tools like Kubeflow, MLflow, DVC, or TensorFlow Serving. • Knowledge of ML model lifecycle management. • Experience with monitoring tools for models (e.g., Prometheus, Grafana, SageMaker Model Monitor). • Strong problem-solving skills and a proactive mindset. • Excellent communication skills to collaborate with technical and non-technical stakeholders. • Good spoken and written English communication skills.

🏖️ Benefits

• Competitive salary and benefits package. • Opportunity to work on cutting-edge AI/ML projects. • A collaborative and innovative work environment. • Professional growth and development opportunities.