Machine Learning Engineer

As an ML Engineer on the team, you will work closely with the Data Science, Engineering, Platform, 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 that include human-in-the-loop components to boost the quality.

The ideal candidate will have a strong background in machine learning model development, deploying large-scale, high-throughput machine learning pipelines to production, and experience with developing and managing frameworks for machine learning platforms, which they can utilize to manage and improve our companys AI/ML initiatives.

Responsibilities

• Contribute to the development of multiple AI-driven end-to-end pipelines that allow for the deployment and scalability of machine learning models.
• Build an end-to-end machine learning platform, covering all lifecycle stages of a model, to ease model development and deployment.
• Build tools and capabilities that help with data ingestion to feature engineering, data management, and organization.
• Deploy cutting-edge algorithms like LLMs, etc., on GPUs along with distributed computing for scalability.
• Contribute to tools and capabilities for model management and model performance monitoring.
• Implement the best engineering practices for scaling ML-powered features to enable the fast iteration of and efficient experimentation with novel features.
• Champion and own the ML infrastructure roadmap, in collaboration with Data Science and other platform teams.
  
  

• Bachelors or masters in computer science or math/stats from a reputed college with 4+ years of experience in solving machine learning engineering problems.
• Prior experience with deploying large-scale machine learning models to production, both in batch and real-time setups.
• Experience with distributed computing frameworks like Spark / Map-Reduce, etc.
• Cloud experience with any one provider (AWS/GCP/Azure).
• Experience with Infra-as-code tools like Terraform.
• Experience and understanding of the entire machine learning pipeline from data ingestion to production.
• Experience with machine learning operations, software engineering, and architecture.
• Experience architecting and building an AI pipeline that supports the productionization of ML models.
• 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 with Databricks is a plus.
• 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.