Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in industries such as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. The role of a Practice Leader is crucial for the company’s Machine Learning practice because it involves not only managing people but also ensuring tasks are completed. A Practice Leader must have sufficient technical experience to uphold and demonstrate best market practices while staying updated with the latest technologies. Requirements: Candidates should combine strong software engineering experience for ML applications with proven people management skills. Strong understanding of ML project lifecycle. Experience in one or more of the following areas: NLP, CV, forecasting, recommender systems, reinforcement learning. Experience in productionizing ML models across different modalities. Experience with developing deep learning models from scratch. Ability to create reusable components for ML pipelines. Ability to justify and explain design decisions. Practical experience with model post-production & maintenance, including monitoring and retraining automation. Experience with cloud platforms such as AWS, open-source alternatives, MLOps platforms, frameworks, and libraries. Strong understanding of Python patterns & best practices. Experience in creating training datasets involving human annotators. Experience working with diverse data sources (OLTP, OLAP, DataLake, Streaming). Practical experience with Spark, Dask, or similar distributed data processing tools. Management Skills: Ability to communicate decisions, status, and roadmaps to technical teams and non-technical stakeholders. Experience in team and department leadership. Skill in relationship and team building to foster collaboration. Ability to mentor and coach team members, supporting their career development. Diplomatic skills, including ethics, empathy, and conflict resolution. Calmness and composure in managing complex or misunderstandings. Responsibilities: Build and lead effective ML engineering teams. Contribute to establishing best practices within the team. Share knowledge and promote a positive team culture. Mentor engineers and team leads, encouraging knowledge sharing. Engage in community activities such as meetups and conferences. Maintain technical excellence and influence across teams and projects. Hire, onboard, and evaluate team members. Identify and address team skill gaps. Improve and maintain team processes. Collaborate with other managers and departments. Align with the company's mission, vision, and values. #J-18808-Ljbffr