Principal MLOps Engineer leading design and optimization of machine learning infrastructure at Wood Mackenzie. Collaborating with data science and engineering teams to ensure robust automated ML lifecycles.
About the role
- Lead Machine Learning Engineer developing AI-powered solutions to transform risk management at Capital One. Collaborating with cross-functional teams to deliver innovative products using advanced machine learning techniques.
Responsibilities
- Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers.
- Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI.
- Fine-tune, develop and evaluate machine learning and foundation models.
- Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities.
- Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One.
- Leverage a broad stack of Open Source and SaaS AI technologies.
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues.
- Retrain, maintain, and monitor models in production.
- Construct optimized data pipelines to feed ML models.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
Requirements
- Bachelor's degree
- At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Go, Scala, or Java
- At least 3 years of experience deploying scalable software solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
Benefits
- comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
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