Capital One Lead Machine Learning Engineer 

India, Karnataka, Bengaluru 

348946382

Voyager (94001), India, Bangalore, Karnataka Lead Machine Learning Engineer

What You’ll Do

● Work with model and platform teams to build systems that ingest large amounts of model and feature metadata that will feed into automated governance decisioning

● Partner with product and design teams to build elegant and scalable solutions to speed up governance processes

● 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

● Advocate for software and machine learning engineering best practices

Function as a technical lead

Basic Qualifications

● Bachelor’s Degree

● At least 6 years of experience designing and building data intensive solutions using distributed computing

● At least 4 years of experience programming with Python, Go, or Java

● At least 2 years of experience building, scaling, and optimizing ML systems

● At least 2 years of experience with the full ML Development Lifecycle using industry-recognized best practices

Preferred Qualifications

● Master’s Degree or PhD in Computer Science, Electrical Engineering, Mathematics, or a similar field

● Atleast 3 years of experience in building production-ready data pipelines that feed ML models

● Atleast 3 years of on job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow

● Atleast 2 years of experience developing performant, resilient, and maintainable code

● Atleast 2 years of experience with data gathering and preparation for ML models, practices, patterns, and automation

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

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

● Contributed to open source ML software

● Authored/co-authored a paper on a ML technique, model, or proof of concep

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