What you will do?

• Design and develop robust backend systems supporting the ML lifecycle, including model training, deployment, and monitoring.
• Contribute to the architecture and build of scalable ML platforms, ensuring performance and efficiency.
• Build and govern the ML lifecycle (data pre-processing, experimentation, training, testing, deployment, monitoring, and maintenance) 
• Build frameworks and tools to enable active learning.
• Build model testing tools and framework.  
• Collaborate with cross-functional teams, including data scientists and product managers, to translate business needs into technical implementations.
• Maintain and improve existing ML systems, optimizing for scalability and reliability.
• Stay on top of the latest developments in ML platforms and backend technologies, applying best practices to our systems.
• Mentor junior engineers and contribute to team growth and skill development

What are we looking for?

• A minimum of 3 years of experience in Machine learning systems.
• Solid hands-on understanding of the ML lifecycle and distributed computing system
• Experience working in a product company, preferably in a role that involved developing ML-driven solutions.
• Proficiency in programming languages and tools relevant to ML engineering (e.g., Python, MLflow, Kubeflow).
• Excellent problem-solving skills, with the ability to innovate and think creatively.
• Strong communication skills and ability to work effectively in a team environment.
• Experience in building ML Platform and related tools such as Human in the loop.
• Experience in monitoring models in production setup.
• Experience in setting feedback loop for model improvement.

What other experience will make you a great candidate?

• Experience in leading large initiatives.
• Previous experience as a Data Scientist is good to have.