Lead Architect - Mlops

RESPONSIBILITIES: As MLOps Engineer, you will work collaboratively with Data Scientists and Data engineers to deploy and operate advanced analytics machine learning models. You ll help automate and streamline Model development and Model operations. You ll build and maintain tools for deployment, monitoring, and operations. You ll also troubleshoot and resolve issues in development, testing, and production environments.Enable Model tracking, model experimentation, Model automation Develop scalable ML pipelinesDevelop MLOps components in Machine learning development life cycle using Model Repository (either of): MLFlow, Kubeflow Model RegistryMachine Learning Services (either of): Kubeflow, DataRobot, HopsWorks, Dataiku or any relevant ML E2E PaaS/SaaSWork across all phases of Model development life cycle to build MLOPS componentsBuild the knowledge base required to deliver increasingly complex MLOPS projects on the Cloud(AWS, Azure, GCP)/On Prem Be an integral part of client business development and delivery engagements across multiple domains.Technical Leadership: As the MLops leader, S/he will support client projects, lead solution design, and contribute to the overall MLOps engineering using on-premise (open-source) or cloud-based (GCP/Azure/AWS) architecturesLooking for candidate with 10-14 Years of experience candidates ideallyAct as a subject matter expert in machine-learning model deployment and MLops pipelines - strong understanding of Machine-learning model building lifecycle, deployment, monitoring and productionResearch and evangelize the use of the data-science and engineering solutions for strategic decision-makingArticulate business value and benefits of the technological solution to senior business and technology partnersResearch, design and implement leading-edge MLops architecture patterns to solve hard problems for the clientsBring data-science engineering thinking to win large AI led digital transformation dealsThought Leadership: Lead and energize the teams and clients by providing thought leadership in enterprise-grade machine-learning operations and modern cloud adoption patternsAct as a Trusted Advisor and build trust with customers through strategic thought, exceptional execution, and technical depth and expertise to help them execute large scale data science/ML projectsBuilding High Performance Culture: Help build and retain high performance (CoE) teamsDevelop people and teams to build the knowledge base required to deliver increasingly complex technology projectsFoster a work environment that is innovative and agile. Hire and retain the best industry talent with right talent management planning.Drive continuous systems improvement, including general administration and management.Help build a culture that is open, transparent, and risk-taking .