Xerox

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 and Shell Scripts.
• Working Experience with container technologies (Docker, Kubernetes).
• Familiarity with standard concepts and technologies used in CI/CD build, deployment pipelines.
• Experience in SQL and 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 skills in scripting languages like Python, Bash, or PowerShell.
• 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)
• Required: Proficiency and experience working with Relational Databases and SQL Scripting (MS SQL Server)
• Above average verbal, written and presentation skills.