Machine Learning Practice Leader

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October 28

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Provectus

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Artificial Intelligence • SaaS

Provectus is an artificial intelligence consultancy and solutions provider that helps businesses transform through AI. Offering both a use case and a platform approach, Provectus integrates AI into organizations to achieve unique business objectives and technical capabilities. Their solutions are cloud-native, vendor-agnostic, and open, allowing for deployment in customer's cloud without restrictive licenses. With applications in industries like retail, manufacturing, and healthcare, Provectus delivers AI-powered use cases and turnkey solutions to drive innovation and efficiency. They also offer consulting, customization, and managed AI services.

501 - 1000 employees

Founded 2012

🤖 Artificial Intelligence

☁️ SaaS

📋 Description

• 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 others to share knowledge. • Have technical excellence and be an influencer in different teams/projects. • Hire and onboard newcomers. • Conduct performance reviews, 1-on-1 meetings. • Identify and address team gaps in knowledge. • Evaluate, improve, and maintain processes. • Сollaborate with other managers across the company. • Communicate and follow the company's mission, vision, and values.

🎯 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 make 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 customer representatives. • Experience in team/department leadership. • Ability to teach and mentor. The role assumes providing employees with their career path and helping them achieve goals. • Diplomatic skills. It means more than just "communication skills" and includes ethics, empathy, compassion, and the ability to resolve conflicts. • Calmness. People are complicated, and you need to be ready for any objectives or misunderstandings.