Python Developer

About Xerox Holdings Corporation

For more than 100 years, Xerox has continually redefined the workplace experience. Harnessing our leadership position in office and production print technology, we've expanded into software and services to sustainably power the hybrid workplace of today and tomorrow. Today, Xerox is continuing its legacy of innovation to deliver client-centric and digitally-driven technology solutions and meet the needs of today's global, distributed workforce. From the office to industrial environments, our differentiated business and technology offerings and financial services are essential workplace technology solutions that drive success for our clients. At Xerox, we make work, work. Learn more about us at www.xerox.com.

Purpose :

• Collaborating with development and operations teams to design, develop, and implement solutions for continuous integration, delivery, and deployment ML-Models rapidly with confidence.
• Use managed online endpoints to deploy models across powerful CPU and GPU machines without managing the underlying infrastructure.
• Package models quickly and ensure high quality at every step using model profiling and validation tools.
• Optimize model training and deployment pipelines, build for CI / CD to facilitate retraining, and easily fit machine learning into your existing release processes.
• Use advanced data-drift analysis to improve model performance over time.
• Build flexible and more secure end-to-end machine learning workflows using MLflow and Azure Machine Learning.
• Seamlessly scale your existing workloads from local execution to the intelligent cloud and edge.
• Store your MLflow experiments, run metrics, parameters, and model artifacts in the centralized workspace.
• Track model version history and lineage for auditability.
• Set compute quotas on resources and apply policies to ensure adherence to security, privacy, and compliance standards.
• Use the advanced capabilities to meet governance and control objectives and to promote model transparency and fairness.
• Facilitate cross-workspace collaboration and MLOps with registries.
• Host machine learning assets in a central location, making them available to all workspaces in your organization.
• Promote, share, and discover models, environments, components, and datasets across teams.
• Reuse pipelines and deploy models created by teams in other workspaces while keeping the lineage and traceability intact.

General :

Technical Skills :

Skills Required

Github, Ms Sql Server, Chef, Agile Methodologies, Networking, Storage, Security, MLops, Docker, Ansible, Team Foundation Server, Mongodb, Puppet, Azure, Kubernetes, Python