Job Description

Key Responsibilities: Lead a team of engineers to build, enhance and support technical platforms that meet the needs of the Data Scientist for operationalizing Machine Learning models including feature engineering, model cataloging, deployment to a compute environment, and model monitoring. Integrate the various technologies part of a platform to insure friction-less user experience Implementation automations to provision and upgrade platforms in AWS Cloud to increase efficiency and speed-to-delivery Understand the MLOps management platform landscape and participate in advancing, optimizing and/or simplifying it Work closely with Platform Architects to define and implement platform roadmaps Participate in proof-of-concepts to evaluate new and emerging technologies relevant to Data Solutions Oversee Incident and problem management Oversee the planning and deployment of platform upgrade Perform platform-related services Troubleshoot advanced deployment and cloud (Relevant to MLOps Platform) infrastructure issues Develop SOP, workbooks and knowledge articles to ensure consistent and reliable platform operations Ensure Data Platforms are scalable, available, reliable, secure and generally meet agreed SLAs Engage with technology partners to further advance the MLOps platforms and to maintain robust in-house knowledge required to successfully fulfill our responsibilities Take part to vendor management Work Experience Combined 8+ years of experience working as a platform/infrastructure engineering, including 3+ years in operations and support experience Certifications BS or MS in Computer Science or related Certification in AWS (Developer/Solution Architect/DevOps/Data Specialty) is a preferable Skills/abilities Hands-on Experience with MLOps technologies ( MLFlow, ModelOps) and Data Science Platforms (Domino Data Lab, AWS Sage maker) Experience with configuration and maintenance of Data Management platforms (Cloudera Data Platform, AWS Data Ecosystem) are a plus Demonstrated ability to support enterprise scale multi-tenant ML Platforms on Cloud Ability to interface with and assist various teams across organization to make best use of the MLOps platform Thorough understanding of and working experience in Cloud eco-system, in particular AWS Experience with CFT/Terraform, Infrastructure as Code (IaC) and configuration languages. Experience with containerization (EKS) based platform deployment with autoscaling. Agile software development. Large-scale distributed software services and solutions. Distributed cloud-computing platforms and technologies like Kubernetes and Spark ecosystem. Programming languages and framework such as SQL, Unix Shell Scripts, Python, Angular, etc. Experience with implementing improvements in areas such as maintainability, scalability, availability, extensibility and security Experience implementing, maintaining, and troubleshooting continuous integration/continuous delivery (CI/CD) Working knowledge of ITSM best practices and tools (e.g. ServiceNow) Working knowledge of information security best practices Operate independently within the guidelines of overall position and objectives Appropriately raise and escalate issues and/or risks and challenge assumptions Demonstrates a strong attention to detail and a commitment to process quality, continuous improvement, and operational excellence. Proven experience of working on application development projects exceptional working knowledge of common SDLC models Proven experience with Incident resolution management (Includes communication with technical and non-technical stakeholders)

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