What You’ll Do 

• Work with model and platform teams to build systems that ingest large amounts of model and feature metadata and runtime metrics to build an observability platform and to make governance decisions. 

• Partner with product and design teams to build elegant and scalable solutions to speed up model governance observability 

• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation big data and machine learning applications.

• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale

• Construct optimized data pipelines to feed machine learning models.

• Use programming languages like Python, Scala, or Java

• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of machine learning models and application code.

Basic Qualifications 

• Bachelor's Degree in Computer Science or a related field

• At least 12 years of experience in software engineering or solution architecture

• At least 10 years of experience designing and building data intensive solutions using distributed computing 

• At least 8  years of experience programming with Python, Go, or Java

• At least 6 years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow

Preferred Qualifications

• Master’s Degree or PhD in Computer Science, Electrical Engineering, Mathematics

• 5+ years of experience building, scaling, and optimizing ML systems

• 5+ years of experience with data gathering and preparation for ML models

• 10+ years of experience developing performant, resilient, and maintainable code.

• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform

• 5+ years of experience with distributed file systems or multi-node database paradigms.

• Contributed to open source ML software

• Authored/co-authored a paper on a ML technique, model, or proof of concept

• 5+ years of experience building production-ready data pipelines that feed ML models.

• Experience in designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

• 5+ years of experience in ML Ops either using open source tools like ML Flow or commercial tools

• 2+ Experience in developing applications using Generative AI i.e open source or commercial  LLMs