Voyager (94001), India, Bangalore, Karnataka
Distinguished Engineer - Machine Learning Engineering
At Capital One India, we work in a fast paced and intellectually rigorous environment to solve fundamental business problems at scale. Using AI / ML, we derive valuable insights about consumer behavior, credit and fraud risk and more from large volumes of data, and use it to build cutting edge patentable products and processes that drive the business forward.
We’re looking for a Distinguished Engineer - Machine Learning Engineering to join the Machine Learning Experience (MLX) team!
The MLX team is at the forefront of how Capital One builds and deploys well-managed AI / ML models and use-cases, both predictive and generative. We drive new innovation and research while working to seamlessly infuse AI / ML into the fabric of the company. The experience we're creating is the foundation that enables each of our businesses to deliver next-generation AI based products and services for our customers.
As a Capital One Distinguished Machine Learning Engineer, in MLX-India, you'll be part of a team that is focused on, amongst other things:
Driving and embedding Observability in our GenAI / ML platforms and products for state of the art performance and reliability
Innovating in responsible use of GenAI to optimize the model development life-cycle including automating monitoring, anomaly detection and root cause analysis
Optimizing developer productivity through AI / Agentic AI including software development life cycle
In this role, you will work on one or more of the above initiatives. You will work with model training and features and serving metadata at scale, to enable automated model governance decisions and to build a model observability platform. 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 and build an observability platform to monitor the models and platform components.
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 design and implement architecture that enables state of the art, next generation big data and machine learning applications.
Leverage cloud-based architectures and technologies to deliver optimized AI / ML models at scale
Construct optimized data pipelines to feed machine learning models.
Use programming languages like Python, Go, 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 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, or a similar field
5+ years of experience building, scaling, and optimizing ML systems
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
Prior experience in MLOps / AIOps
Experience in developing applications using Generative AI or Agentic AI tools (open source or commercial)
Contributed to open source ML software
Authored/co-authored a paper on a AI / ML technique, model, or proof of concept