Senior ML Engineer Lead - Time Series

Looking for a highly skilled Senior Machine Learning Engineering Lead to oversee and drive machine learning initiatives focusing on time series analysis, process curve analysis, tabular data, and feature engineering. In this role, you will lead a team of engineers and data scientists, ensuring the effective delivery of machine learning solutions to customers. You will also be responsible for designing efficient workflows, building robust CI/CD pipelines, and handling client interactions to deliver high-quality solutions on time. Roles Responsibilities:

Machine Learning and Data Engineering:

Leadership and Team Management:

Pipeline and Workflow Design:

Required skills:

Soft Skills:

• Strong leadership and team management skills, with a focus on mentoring and development of team members.
• Excellent communication skills for handling customer interactions, explaining technical concepts to non-technical stakeholders, and delivering presentations.
• Problem-solving mindset with the ability to analyze complex data and identify actionable insights.
• Highly organized, detail-oriented, and able to manage multiple projects simultaneously.

Experience:

• 8+ years of experience in machine learning engineering with a focus on time-series analysis, process curve analysis, tabular data, and feature engineering.
• At least 3-5 years of leadership experience managing teams and handling customer-facing responsibilities.
• Strong experience in designing and deploying ML models in production environments.
• Proven track record of successfully managing client relationships and delivering high-quality solutions on time.
• Experienced in working in cross-functional, international setups
• Entrepreneurial, business-driven mindset.

Preferred Expertise:

• Experience in deploying models at scale using containerization technologies like Docker and Kubernetes.
• Knowledge of MLOps principles and practices.
• Background in domain-specific areas (e. g. , manufacturing, finance, healthcare) related to time-series and process data.