| Lead Data Scientist ML Ops || Atlanta, GAHybrid at Atlanta, Georgia, USA |
| Email: [email protected] |
| From: jatin parashar, Vyze Inc [email protected] Reply to: [email protected] Job Description - Title: Lead Data Scientist ML Ops Duration: C2H (3- 6 months contract to hire) Location: Atlanta, GA Hybrid As the ML Ops Lead, you will play a critical role in bridging the gap between data science and production. You will be responsible for implementing and managing the infrastructure, processes, and tools necessary to deploy, monitor, and maintain machine learning models in a production environment. You will work closely with data scientists, engineers, and other stakeholders to ensure the seamless integration of machine learning solutions into our products and services. Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Proven experience in ML Ops or a similar role, with a deep understanding of machine learning and software engineering principles. Strong knowledge of containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP). Experience with Software as a Service platforms to include SAS, C3.ai, and AWS Sagemaker Proficiency in programming languages such as Python, and experience with automation and scripting. Familiarity with machine learning frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn). Excellent problem-solving skills and the ability to troubleshoot complex issues in a production environment. Strong communication and collaboration skills. Experience with DevOps practices and tools is a plus. Opportunity to work on cutting-edge projects in the AI and ML space. Key Responsibilities: ML Model Deployment: Lead the deployment of machine learning models into production environments, ensuring they are scalable, reliable, and maintainable. Infrastructure Management: Collaborate with the IT and DevOps teams to provision and manage the necessary infrastructure, including cloud resources, containers, and data pipelines, to support machine learning workloads. Automation: Develop and maintain automation scripts and tools for model deployment, monitoring, and retraining, with a focus on efficiency and reproducibility. Model Monitoring: Implement robust monitoring and alerting systems to track model performance, data drift, and anomalies, and take proactive steps to address issues as they arise. Security and Compliance: Ensure that machine learning models and data pipelines adhere to security and compliance standards, and work closely with security teams to address any vulnerabilities or risks. Collaboration: Collaborate with data scientists and engineers to understand model requirements, and work together to optimize models for deployment. Documentation: Maintain clear and comprehensive documentation of ML Ops processes, procedures, and configurations. Continuous Improvement: Stay up to date with industry best practices and emerging technologies in ML Ops, and proactively identify opportunities to enhance the ML Ops workflow. Team Leadership: Mentor and guide junior ML Ops engineers and contribute to their skill development. Keywords: artificial intelligence machine learning information technology Georgia |
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| Mon Feb 05 22:13:00 UTC 2024 |