Backfill Needed Asap!Must have strong AI/ML and Azure, and data warehousingRemoteNeed to validate projects worked on, technologies used, and why/how they used the technologies.Past candidates were rejected for soft skills. They didn't listen to questions and interrupted the interviewer. They didn't show an ability to lead a team.
The resumes were technically on point.Location: 100% telecommuteProject: We are establishing Agile Data Warehouse in cloud and many new AI practices to enable personalization in various capabilities to improve employee experience.The roleYou will lead a team of Data Engineers and Data Scientists and be their single point of contact for technical clarification and impediment resolution. Participate in sessions with business stakeholders to translate business objectives into clearly defined AI and Analytical projects. You will create and maintain the master architecture representation and create a modular and scalable solution design.Primary responsibilities:Identify opportunities for Data Engineering and AI to enhance the core product platform, select the best machine learning techniques to the specific business problem and then build the models that solve the problem.Architect and design AI/ML and Analytics solutions and cloud servicesOwn the end-to-end process, from recognizing the problem to implementing the solution.Establish DataOps and MLOps principles and best practicesTop 3 skillsAI/ML solution designStrong problem solving and troubleshooting skills with the ability to exercise mature judgment.MLOpsIdeal background:Hands on experience with modern application – microservices, Cloud and CI/CD5-7 years of hands on Data and AI engineering workGood communication with developing architecture and design documentationRequired:Bachelor's degree or master's degree in Computer Science.5+ years of hands-on software engineering experience.Demonstrated AI/ML solution design experienceProven work experience in Spark, Python, SQL, Any RDBMS.Familiarity with Azure Data Lake, Synapse, ADF, Power BI.Experience building, deploying and maintaining ML models in productionExperience with MLOps tools such as ModelDB, MLFlow and Kubeflow.Familiar with best practices in the data engineering and MLOps community.Ability to convey complex concepts and ideas in a clear and concise manner to a wide range of audience internal business stakeholders, outside partners and technology teams.To be able to work in a fast-paced agile development environment.Proven track record in working with diverse teams to achieve goalsStrong problem solving and troubleshooting skills with the ability to exercise mature judgment.