Machine Learning Engineer (ai Foundations)

Capital One Capital One · Banking · McLean, VA +1

Machine Learning Engineer focused on productionizing AI/ML applications and systems at scale within a fintech company. The role involves designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, and collaborating in an Agile team. Responsibilities include retraining, maintaining, and monitoring models in production, leveraging cloud-based architectures, constructing data pipelines, and ensuring CI/CD best practices, code security, and responsible AI. Basic qualifications include a Bachelor's degree and experience in data-intensive solutions and programming. Preferred qualifications include cloud experience, open-source contributions, and building production-ready data pipelines.

What you'd actually do

  1. Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
  2. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)
  3. Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
  4. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
  5. Retrain, maintain, and monitor models in production

Skills

Required

  • Bachelor's Degree
  • 2 years of experience designing and building data-intensive solutions using distributed computing
  • 2 years of experience programming with Python, Scala, or Java
  • 1 year of Machine Learning experience with an industry recognized ML framework (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)

Nice to have

  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • 1+ years of experience working with large code bases in a team environment
  • 1+ years of experience with distributed file systems or multi-node database paradigms
  • Contributed to open source ML software
  • 1+ years of experience building production-ready data pipelines that feed ML models
  • Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • advanced LLMs and autonomous agentic systems
  • operational efficiency
  • trustworthy and scalable systems
  • designing and building data-intensive solutions
  • programming with Python, Scala, or Java
  • Machine Learning experience with an industry recognized ML framework
  • developing and deploying ML solutions in a public cloud
  • building production-ready data pipelines that feed ML models

Other signals

  • productionizing machine learning applications and systems at scale
  • developing advanced LLMs and autonomous agentic systems
  • operational efficiency
  • trustworthy and scalable systems