DevOps

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114 jobs found

1mo ago

AI/ML Lead Data Engineer - Automation/Image Processing

JPMorganChase Tampa, FL

Tampa, FL

1mo ago

Full time Analytics Artificial Intelligence AWS

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Job Description

The AI/ML Lead Data Engineer focusing on Automation and Image Processing is a senior technical leadership role based onsite in Tampa, FL, within JPMorganChase's Commercial & Investment Bank. The position centers on designing, building, and maintaining scalable data pipelines and architectures for ingesting, processing, and storing scanned document images and OCR outputs, in collaboration with data scientists and ML engineers to support analytics, model training, and production-grade workflows.

Responsibilities

Architect and maintain scalable, high‑performance data pipelines and infrastructure to ingest, process, and store large volumes of scanned document images across enterprise workflows.
Design end‑to‑end data solutions on AWS to enable seamless flow from source systems through OCR processing, model inference, and downstream data extraction and categorization pipelines.
Develop robust image preprocessing and OCR integration pipelines handling TIF and PNG conversions, normalization, resolution enhancement, noise reduction, and batching for downstream computer vision and OCR models.
Build and optimize pipelines that integrate OCR outputs, extracting structured text and metadata and routing them into databases and analytics platforms for further processing.
Design and manage data storage architectures and containerized deployments using Oracle databases and AWS stores (S3, EFS) to catalog, index, and retrieve extracted text, classifications, and metadata from processed images.
Drive containerized deployment practices with AWS EKS to deploy and scale image processing microservices, OCR engines, and data pipeline components with high availability and fault tolerance.
Collaborate with data scientists and ML engineers to ensure training datasets for various models are curated, versioned, labeled, and accessible through well-structured data pipelines.
Evaluate and integrate emerging data technologies to improve pipeline throughput, reduce processing latency for high‑volume document workloads, and optimize cost efficiency.
Establish data quality, lineage, governance, and security frameworks to ensure traceability and integrity of extracted data throughout the processing lifecycle.
Partner with security and compliance teams to ensure scanned document data, including sensitive content, is handled per regulatory requirements, encryption standards, and access controls.
Lead and mentor a team of data engineers, establishing coding standards, peer review processes, CI/CD workflows, and best practices for production‑grade image and document processing pipelines.

Requirements

Formal training or certification in data engineering with 5+ years of applied experience.
Strong proficiency in Java, Groovy, and Python for building data pipelines, image preprocessing workflows, automation scripts, and backend services.
Hands‑on experience with image file handling, especially TIF/PNG processing, multi‑page document splitting, format conversion, and integration with OCR and computer vision pipelines.
Deep experience with AWS cloud services including S3, Lambda, Step Functions, and CloudWatch for scalable data workflows.
Expertise in AWS EKS for deploying and managing containerized image processing, OCR, and data pipeline services using Docker and Kubernetes.
Advanced knowledge of Oracle databases, including PL/SQL, performance tuning, partitioning strategies, and data modeling for large volumes of extracted document data and classifications.
Familiarity with OCR technologies and the ability to structure OCR output for downstream analytics and model training.
Understanding of data requirements for training deep learning models, including dataset preparation, annotation management, and feature store integration.
Experience with CI/CD pipelines (Jenkins) and infrastructure‑as‑code tools (Terraform, CloudFormation) for automated deployment and environment management.
Strong understanding of data governance, data quality, metadata management, and data cataloging, especially in document‑centric and image‑heavy data ecosystems.
Excellent leadership, communication, and stakeholder management skills for guiding technical decisions across cross‑functional teams.

TECHNOLOGIES

Java, Groovy, Python
AWS S3, Lambda, Step Functions, CloudWatch
AWS EKS, Docker, Kubernetes
Oracle Database, PL/SQL, EFS
Jenkins, Terraform, CloudFormation
OCR technologies

BENEFITS

Comprehensive health care coverage
On-site health and wellness centers
Retirement savings plan
Backup childcare
Tuition reimbursement
Mental health support
Financial coaching
Commission-based pay
Discretionary incentive compensation (cash and/or forfeitable equity)

PREFERRED QUALIFICATIONS, CAPABILITIES, AND SKILLS

Domain expertise in the healthcare industry

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1mo ago

Lead Machine Learning Engineer (Enterprise Platforms Technology)

Capital One McLean, VA

McLean, VA

1mo ago

$197k - $225k/yr Full time AWS Azure Big Data

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Job Description

This on-site role in McLean, VA focuses on productionizing ML applications and systems at scale within Capital One's Enterprise Platforms Technology group.

Responsibilities

Design, build, and deliver ML models and components that address real world business problems, in collaboration with Product and Data Science teams.
Inform ML infrastructure decisions through understanding of modeling techniques and issues, including model selection, data, feature engineering, model training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
Collaborate within a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code is well governed to minimize vulnerabilities, and that models meet risk and Responsible AI / Explainable AI standards.
Use programming languages such as Python, Scala, or Java.

Requirements

Bachelor’s Degree.
At least 6 years of experience designing and building data-intensive solutions with distributed computing (internship experience does not apply).
At least 4 years of experience programming with Python, Scala, or Java.
At least 2 years of experience designing, scaling, and optimizing ML systems.

Technologies

Python, Scala, Java
scikit-learn, PyTorch, Dask, Spark, TensorFlow
AWS, Azure, Google Cloud Platform

Benefits

Health benefits
Financial benefits
Performance-based incentive compensation (cash bonuses and/or long-term incentives)

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1mo ago

Senior Lead Machine Learning Engineer

Capital One McLean, VA

McLean, VA

1mo ago

$230k - $262k/yr Full time AWS Azure Ml Cloud Architecture

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Job Description

Capital One is seeking a Senior Lead Machine Learning Engineer to drive the productionization of ML applications at scale within an Agile team. The role centers on shaping ML architecture, reviewing model and application code, and ensuring the high availability and performance of ML systems in production.

Location: McLean, VA (onsite)

Salary: USD 229,900 - 262,400 per year

Responsibilities

Design, build, and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams.
Inform ML infrastructure decisions with a solid understanding of modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Develop and test application code, train and validate ML models, and automate tests and deployment.
Collaborate within a cross-functional Agile team to create and enhance software enabling state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production environments.
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure reliable deployment of ML models and application code.
Ensure code quality and governance to reduce vulnerabilities, uphold risk standards, and follow Responsible and Explainable AI practices.
Work with programming languages such as Python, Scala, or Java.

Requirements

Bachelor's Degree
At least 8 years of experience designing and building data-intensive solutions using distributed computing; internship experience does not count toward this requirement.
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 leading teams developing ML solutions

Technologies

Python, Scala, Java
scikit-learn, PyTorch, Dask, Spark
Kubeflow, TensorFlow
AWS, Azure, Google Cloud Platform

Benefits

Performance-based incentive compensation
Health benefits
Financial benefits

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1mo ago

Sr. Distinguished Machine Learning Engineer

Capital One McLean, VA

McLean, VA

1mo ago

$315k - $359k/yr Full time Senior Cloud Architecture DevOps

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Job Description

Capital One offers a performance-based incentive program with cash bonuses and long-term incentives. This onsite, McLean, VA opportunity places you in a senior leadership role focused on bringing machine learning to production at scale. You will guide architecture and modeling efforts, oversee the development of robust ML systems, and mentor engineers across cross-functional teams.

Responsibilities

Deliver machine learning models and software components that address complex business problems in financial services, collaborating with Product, Architecture, Engineering, and Data Science teams.
Shape and advance ML models and supporting software to enable advanced, intelligent systems.
Lead large scale ML initiatives with the end user in mind.
Utilize cloud based architectures and technologies to deploy optimized ML models at scale.
Refine data pipelines to feed ML models efficiently.
Work with Python, Scala, and C or C++ across development tasks.
Leverage compute frameworks such as Dask and RAPIDS to accelerate processing.
Promote best practices across engineering and modeling lifecycles.
Contribute to recruiting, developing, and retaining top engineering talent.

Requirements

Bachelor’s degree.
At least 10 years designing and building data‑intensive solutions using distributed computing.
At least 7 years programming in C, C++, Python, or Scala.
At least 4 years of experience with the full ML development lifecycle in a business critical setting.

Technologies

Python
Scala
C/C++
Dask
RAPIDS
NumPy
Pandas
Scikit-learn

Benefits

Performance based incentive compensation (cash bonuses and/or long term incentives)

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1mo ago

Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS)

Capital One Richmond, VA

Richmond, VA

1mo ago

$179k - $205k/yr Full time AWS Big Data Cloud

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Job Description

Capital One is seeking a Lead Machine Learning Engineer to scale ML solutions across the organization, focusing on architecture, model development, deployment, and operations within an Agile environment. This role centers on productionizing ML applications and systems, building robust data pipelines, and delivering ML capabilities at scale.

Location

Richmond, VA (onsite)

Compensation

Salary: USD 179,400 - 204,700 per year

Responsibilities

Design, build, and deliver ML models and components that address real-world business problems, partnering with Product and Data Science teams.
Inform ML infrastructure decisions using knowledge of modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
Collaborate within a cross-functional Agile team to create and improve software for state-of-the-art big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and code.
Ensure code quality and governance, manage risk for models, and adhere to Responsible and Explainable AI practices.
Use programming languages such as Python, Scala, or Java.

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, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

KServe, Kubernetes, PyTorch, TensorFlow
AWS, Python, Scala, Java
scikit-learn, Dask, Spark

Benefits

This role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI). Incentives can be discretionary or non-discretionary depending on the plan.
Health, financial, and other benefits that support total well-being.

Team Description

The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in res

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1mo ago

Lead Machine Learning Engineer

Capital One New York, NY

New York, NY

1mo ago

$215k - $246k/yr Full time Analytics Artificial Intelligence AWS

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Job Description

Capital One's Luxury Travel team seeks a Lead Machine Learning Engineer to scale ML applications from concept to production and design architectures that deliver tangible impact. Working onsite in New York, NY, you'll join an Agile team focused on turning data into sophisticated ML solutions for high-end travelers. A Bachelor’s Degree is required, with a listed minimum of 2 years of experience, while formal requirements specify at least 6 years designing and building data-intensive solutions. The role offers a yearly salary between USD 215,200 and 245,600.

Responsibilities

Design, build, and/or deliver ML models and components that solve real-world business problems, while collaborating with the Product and Data Science teams
Inform ML infrastructure decisions using your understanding of modeling techniques and issues, including model choice, data, feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation
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 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
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
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
Use programming languages like Python, Scala, or Java

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, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems

Technologies

Python, Scala, Java, scikit-learn, PyTorch, Dask, Spark, TensorFlow, AWS, Azure, Google Cloud Platform

Benefits

Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Health benefits
Financial benefits
Other benefits

Team Description

In the Luxury Travel organization, you will architect the intelligent systems that power Capital One Travel's vision for the high-end market. You'll have the unique opportunity to transform complex data into bespoke, anticipatory experiences for our most discerning travelers. If you are a visionary technologist who thrives at the intersection of cutting-edge AI and human-centric design, and you're driven to redefine the gold standard of travel.

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1mo ago

Senior Lead Machine Learning Engineer

Capital One McLean, VA

McLean, VA

1mo ago

$230k - $262k/yr Full time Artificial Intelligence AWS Deep Learning

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Job Description

Capital One seeks a Senior Lead Machine Learning Engineer to operationalize machine learning applications at scale within an Agile environment, focusing on ML architecture and end-to-end development and deployment, with an emphasis on responsible and explainable AI.

Compensation

Salary range: USD 229,900 to 262,400 per year.

Responsibilities

Design and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams.
Guide ML infrastructure decisions based on modeling techniques, including model selection, data and feature choices, training, hyperparameter tuning, dimensionality considerations, bias and variance, and validation.
Tackle complex problems by developing and testing application code, building and validating ML models, and automating tests and deployment.
Join a cross-functional Agile team to create software enabling cutting edge big data and ML applications.
Retrain, monitor, and maintain models in production environments.
Leverage or construct cloud based architectures, technologies, and platforms to deliver optimized ML models at scale.
Build optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code is well governed to minimize vulnerabilities and that models comply with risk considerations and Responsible and Explainable AI practices.
Proficient in programming languages such as Python, Scala, or Java.

Requirements

Bachelor’s Degree.
Minimum eight years designing and delivering data-intensive solutions using distributed computing, with internships not counted.
At least four years programming with Python, Scala, or Java.
At least three years building, scaling, and optimizing ML systems.
At least two years leading teams developing ML solutions.

Technologies

Python
Scala
Java
AWS
Azure
Google Cloud Platform
scikit-learn
PyTorch
Dask
Spark
TensorFlow

Benefits

Performance-based incentive eligibility
Health, financial and other benefits that support total well being

Basic Qualifications

Bachelor’s Degree
Minimum eight years designing and delivering data-intensive solutions using distributed computing (internships excluded)
At least four years programming with Python, Scala, or Java
At least three years building, scaling, and optimizing ML systems
At least two years leading teams developing ML solutions

Preferred Qualifications

Master's or doctoral degree in computer science, electrical engineering, mathematics, or a related field
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
4+ years of on-the-job experience with industry recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
3+ years building performant, resilient, and maintainable code
3+ years of experience gathering and preparing data for ML models
3+ years of people management experience
Impact in the ML field through conferences, papers, blog posts, open source contributions, or patents
3+ years building production-ready data pipelines that feed ML models
Ability to communicate complex technical concepts clearly to diverse audiences