Lead Full-cycle Development: Take charge of the complete lifecycle of machine learning models, overseeing conception, prototyping, deployment, and iterative optimization processes to enhance performance and scalability.
Architect Solutions: Utilize strong software design principles to build efficient and maintainable ML applications and services that adhere to best industry practices.
Collaborative Model Development: Partner with data scientists and stakeholders to address specific business challenges, thoroughly exploring available data for effective model training and evaluation.
Implement Data Pipelines: Design complex data processing workflows to ensure efficient feature extraction and model training phases are streamlined.
Champion MLOps Practices: Establish solid frameworks for MLOps, including model monitoring, Continuous Integration/Continuous Deployment (CI/CD) processes, and best practices for maintaining production models effectively.
Enhance Performance & Scalability: Diagnose and improve the efficiency of deployed models while preparing systems for increased transactional demands.
Mentor & Collaborate: Collaborate across diverse teams, nurturing a culture of knowledge-sharing and mentorship while advocating for innovative practices.
Stay Ahead of Trends: Keep abreast of the evolving landscape of machine learning and AI technologies, exploring, trialing, and incorporating transformative tools and methods.
Documentation & Reporting: Meticulously document processes, model specifications, and workflows to guarantee clear communication and knowledge retention across the team
Role & responsibilities
Preferred candidate profile

Keyskills: ML OPs AI Ci/Cd Machine Learning