As a Lead ML Engineer in the Data Science team, you will work closely with the engineering, product, and operations teams to build state-of-the-art ML based solutions for B2B SaaS products. This will entail applying advanced ML algorithms at scale for core products and developing robust end-to-end production pipelines, which include a Human-in-the-Loop component to boost the quality. In this role, you will find innovative ways to apply emerging technologies to improve business processes and drive more value for customers. The ideal candidate will have a strong background in machine learning model development, natural language processing and understanding, and data analysis, which they can utilize to manage and improve our company's AI/ML initiatives.
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.
This job was posted by Akash R from CommerceIQ.
