ML Practice Leader

APractice Leaderis the first point of contact for their direct reports, and they liaise and relay information between senior leaders, HR, and engineers.The role of aPractice Leaderis probably the most important because this is the first line of people management, and it provides things to be done. On the one hand, a PL is a part of the Practice, so this person must be at least senior-level in their specialty. On the other hand, aPractice Leaderis a people manager.The complete list of requirements looks like the following:Practical experience and strong understanding of Python patterns & best practicesStrong understanding of ML project lifecyclePractical experience with creating training datasets involving human annotatorsExperience with writing Deep Learning models from scratchExperience in >1 of the following areas: NLP, CV, forecasting, recommender systemsStrong experience with agentic workflows, RAG architecture, and GraphRAGExperience and in-depth understanding of transformersPractical experience with /AWS/other cloud/open source alternatives/ MLOps platforms, frameworks, and librariesPractical experience with model post-production & maintenance: model and data monitoring, retraining automation, etcAbility to make reusable components of ML pipelinesPractical experience with a variety of data sources (OLTP, OLAP, DataLake, Streaming)Experience in DataOps or ML/MLOps would be a significant plusAbility to explain decisions, status, and roadmap to non-technical customer representativesExperience in team/department leadershipAbility to teach and mentor. The role assumes providing employees with their career path and helping them achieve goalsDiplomatic skills. It means more than just "communication skills" and includes ethics, empathy, compassion, and the ability to resolve conflictsCalmness. People are complicated, and you need to be ready for any objectives or misunderstandingsResponsibilities:Build effective teamsParticipate in meetups, conferences, and build communityShare best practices and culture with the teamMentor engineers, coach Team Leads, and encourage others to share knowledgeHave technical excellence and be an influencer in different teams/projectsHire and onboard newcomersConduct performance reviews, 1-on-1 meetingsIdentify and address team gaps in knowledgeEvaluate, improve, and maintain processesСollaborate with other managers across the companyCommunicate and follow the company's mission, vision, and valuesWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.