Xerox

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 :

• Builds knowledge of the organization, processes and customers.
• Requires knowledge and experience in own discipline; still acquiring higher level knowledge and skills.
• Receives a moderate level of guidance and direction.
• Moderate decision-making authority guided by policies, procedures, and business operations protocol.

Technical Skills :

• Will need to be strong on ML pipelines, modern tech stack.
• Proven experience with MLOPs with Azure and MLFlow etc.
• Experience with scripting and coding using Python.
• Working Experience with container technologies (Docker, Kubernetes).
• Familiarity with standard concepts and technologies used in CI/CD build, deployment pipelines.
• Experience in relational database (e.g.:- MS SQL Server) & NoSQL Databases (e.g.:- MongoDB) Python and Strong math skills (e.g. statistics).
• Problem-solving aptitude and Excellent communication and presentation skills.
• Automating and streamlining infrastructure, build, test, and deployment processes.
• Monitoring and troubleshooting production issues and providing support to development and operations teams.
• Managing and maintaining tools and infrastructure for continuous integration and delivery.
• Managing and maintaining source control systems and branching strategies.
• Strong knowledge of Linux/Unix administration.
• Experience with configuration management tools like Ansible, Puppet, or Chef.
• Strong understanding of networking, security, and storage.
• Understanding and Practice of AGILE Methodologies.
• Proficiency and experience in working as part of the Software Development Lifecycle (SDLC) using Code Management & Release Tools (MS DevOps, Github, Team Foundation Server)
• Above average verbal, written and presentation skills.

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