ML Practice Leader

Practice Leader is the first point of contact for their direct reports, and they liaise and relay information between senior leaders, HR, and engineers. The role of a Practice Leader is probably the most important because this is the first line of people management, and it provides people with the things they need to do. A Practice Leader must be at least senior‑level in their specialty and is also a people manager. Requirements Practical experience and strong understanding of Python patterns & best practices Strong understanding of ML project lifecycle Practical experience with creating training datasets involving human annotators Experience with writing Deep Learning models from scratch Experience in >1 of the following areas: NLP, CV, forecasting, recommender systems Strong experience with agentic workflows, RAG architecture, and GraphRAG Experience and in-depth understanding of transformers Practical experience with AWS/other cloud/open‑source alternatives, MLOps platforms, frameworks, and libraries Practical experience with model post‑production & maintenance: model and data monitoring, retraining automation, etc. Ability to build reusable components of ML pipelines Practical experience with a variety of data sources (OLTP, OLAP, DataLake, Streaming) Experience in DataOps or ML/MLOps would be a significant plus Ability to explain decisions, status, and roadmap to non‑technical customers Experience in team/department leadership Ability to teach and mentor; provide employees with their career path and help them achieve goals Diplomatic skills: ethics, empathy, compassion, and conflict resolution Calmness in managing complex people dynamics Responsibilities Build effective teams Participate in meetups, conferences, and build community Share best practices and culture with the team Mentor engineers, coach Team Leads, and encourage knowledge sharing Maintain technical excellence and influence across teams/projects Hire and onboard newcomers Conduct performance reviews and 1‑on‑1 meetings Identify and address team gaps in knowledge Evaluate, improve, and maintain processes Collaborate with other managers across the company Communicate and follow the company's mission, vision, and values We 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. Seniority Level Mid‑Senior level Employment Type Full‑time Job Function Other Industries Transportation, Logistics, Supply Chain and Storage #J-18808-Ljbffr