BENGALURU · FULLTIME
Lead Software Engineer
Capital One
Bengaluru · onsite · Posted 56d ago
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Not disclosed8–13 yrsfulltimeBengaluru
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Section · 01
About this role
- As a Capital One Machine Learning Engineer, youll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale
- You ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms
- Working within an Agile environment, you ll serve as a technical lead, helping guide machine learning architectural design decisions, developing and reviewing model and application code, and ensuring high availability and performance of our machine learning applications
- Youll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering
- You ll also mentor other engineers and further develop your technical knowledge and skills to keep Capital One at the cutting edge of technology
- What you ll do in the role: Deliver ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- 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 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 MLmodels
- Use programming languages like Python, Scala, or Java
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Advocate for software and machine learning engineering best practices
- Function as the engineering tech lead for large-scale initiatives
- Recruit and mentor junior engineering talent
- Basic Qualifications Bachelor s degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience with the full ML development lifecycle using modern technology in a business critical setting
- At least 2 years of experience leading teams developing ML solutions
- Preferred Qualifications Masters or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
- At least 4 years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
- At least 3 years of experience developing performant, resilient, and maintainable code
- At least 3 years of experience with data gathering and preparation for ML models
- At least 3 years of people management experience
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- At least 3 years of experience building production-ready data pipelines that feed ML models
- Ability to communicate complex technical concepts clearly to a variety of audiences
- At this time, Capital One will not sponsor a new applicant for employment authorization for this position
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Section · 02
Skills
DockerKubernetesrestful apisSpring BootJavaCi/Cd
Section · Company
About CapitalOne
Capital One
FinTech
175
employees
1994
32 years old
McLean
United States
About
Capital One helps customers find the right credit cards, checking or savings accounts, auto loans, and other banking services for them & their businesses.
Industries
FinTechBanking / Insurance / AccountingFinancial Services
Employee ratings
69 reviews
3.5/ 5
Culture
3.5
Career growth
2.9
Work-life
3.7
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