Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.
About the role
- Machine Learning Engineer at Capital One working on productionizing ML applications in Agile teams. Focused on technical design, development, and implementation with cutting-edge technology.
Responsibilities
- Part of an Agile team dedicated to productionizing machine learning applications and systems at scale
- Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms
- Focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications
- Continuously learn and apply the latest innovations and best practices in machine learning engineering
- Design, build, and/or deliver ML models and components that solve real-world business problems
- Inform ML infrastructure decisions using understanding of ML modeling techniques
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Collaborate as part of a cross-functional Agile team to create and enhance software for big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models.
Requirements
- Bachelor’s Degree
- At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)
- At least 3 years of experience designing and building data-intensive solutions using distributed computing
- At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
- At least 1 year of experience productionizing, monitoring, and maintaining models
- 1+ years of experience building, scaling, and optimizing ML systems
- 1+ years of experience with data gathering and preparation for ML models
- 2+ 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
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
- 3+ years of experience with distributed file systems or multi-node database paradigms
- Contributed to open source ML software
- Authored/co-authored a paper on a ML technique, model, or proof of concept
- 3+ years of experience building production-ready data pipelines that feed ML models
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Benefits
- Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
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