Job
Description
We're looking for a Lead - Machine Learning Engineer to join the Machine Learning Experience (MLX) team! As a Capital One Machine Learning Engineer (MLE), you'll be part of a team focusing on automating governance within the model development lifecycle. You will work with model training and feature and serving metadata at scale, to enable automated model governance decisions. You will contribute to building a system to do this for Capital One models, accelerating the move from fully trained models to deployable model artifacts ready to be used to fuel business decisioning. The MLX team is at the forefront of how Capital One builds and deploys we'll-managed ML models and features. We onboard and educate associates on the ML platforms and products that the whole company uses. We drive new innovation and research and we're working to seamlessly infuse ML into the fabric of the company. The ML experience were creating today is the foundation that enables each of our businesses to deliver next-generation ML-driven products and services for our customers. 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 Bachelors 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 Masters 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 Candidates should have experience with Model Observability - Monitoring and Telematics, MLOps, Python Scripting, Stats and Mathematical Modelling and Data Pipelines. Should have developed some POC/ Production Grade Application implementing Use Case specific Fine Tuned LLM. Candidates should have Maintained/ Developed 2-3 Large Scale Production grade ML Applications involving a set of various components using different ML/ AI models i.e mix of Calssic ML models as we'll as Advanced ML Models. Integration with LLMs like LLAMA, BERT or any other transformer based model is a great plus point. 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 concept
