Lead ML Engineer

Responsibilities:

• Architect and Own multiple AI driven end-to-end pipelines that allow for deployment and scalability of machine learning models.
• Build tools and capabilities that help with data ingestion to feature engineering, data management, and organization.
• Build tools and capabilities for scalability through distributed optimization.
• Deploy cutting-edge algorithms like LLMs, etc. on GPUs at scale.
• Work with stakeholders on the research and software engineering side of the company to understand how to support their teams.
• Build tools and capabilities for model management and model performance monitoring.
• Build operating services at scale with high availability and reliability.
• Propose and implement the best engineering practices for scaling ML-powered features, with a goal to enable the fast iteration of and efficient experimentation with novel features.
• Contribute to and influence the ML infrastructure roadmap in collaboration with the Data Science team.

Requirements:

• Bachelor's or Master's in Computer Science or Math/Stats from a reputed college with 5+ years of experience in solving machine learning engineering problems.
• Experience and understanding of the entire machine learning pipeline from data ingestion to production.
• Experience with machine learning operations, software engineering, and architecture.
• Experience with large-scale systems including parallel computing and GPUs.
• Experience architecting and building an AI pipeline that supports productionization of ML models.
• Experience with MLOps systems.
• Strong programming skills in a scientific computing language such as Python or SQL.
• Experience using frameworks for machine learning and data science like scikit-learn, pandas, and NumPy.
• Experience working with ML tools such as Tensorflow, Keras, and Pytorch.
• Ability to take successful, complex research ideas from experimentation to production.
• Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams.
  • Good depth and breadth in machine learning (theory and practice), optimization methods, data mining, statistics, and linear algebra.