... data technologies and tools to continuously improve pipeline throughput, reduce processing latency ... preparation, annotation management, and feature store integration • Experience with CI/CD ...
... data technologies and tools to continuously improve pipeline throughput, reduce processing latency ... preparation, annotation management, and feature store integration • Experience with CI/CD ...
AI/ML Lead Data Engineer - Automation/Image Processing
Tampa, FL · On-site
$96K - $127K/yr
Evaluate and integrate emerging data technologies and tools to continuously improve pipeline ... annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
AI/ML Lead Data Engineer - Automation/Image Processing
Tampa, FL · On-site
$96K - $127K/yr
Evaluate and integrate emerging data technologies and tools to continuously improve pipeline ... annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
Evaluate and integrate emerging data technologies and tools to continuously improve pipeline ... annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
Evaluate and integrate emerging data technologies and tools to continuously improve pipeline ... annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
Evaluate and integrate emerging data technologies and tools to continuously improve pipeline ... annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
Evaluate and integrate emerging data technologies and tools to continuously improve pipeline ... annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
... tech who provides quality patient care when performing diagnostic general/or vascular sonographic ... Data Analysis - Analyze acquired images and recordings to identify abnormalities, measurements, and ...
... tech who provides quality patient care when performing diagnostic general/or vascular sonographic ... Data Analysis - Analyze acquired images and recordings to identify abnormalities, measurements, and ...
... tech who provides quality patient care when performing diagnostic general/or vascular sonographic ... Data Analysis - Analyze acquired images and recordings to identify abnormalities, measurements, and ...
... tech who provides quality patient care when performing diagnostic general/or vascular sonographic ... Data Analysis - Analyze acquired images and recordings to identify abnormalities, measurements, and ...
... tech who provides quality patient care when performing diagnostic general/or vascular sonographic ... Data Analysis - Analyze acquired images and recordings to identify abnormalities, measurements, and ...
... tech who provides quality patient care when performing diagnostic general/or vascular sonographic ... Data Analysis - Analyze acquired images and recordings to identify abnormalities, measurements, and ...
Data Annotation Tech information
See Seminole, FL salary details
$11.39 - $13.29
3% of jobs
$13.29 - $15.20
19% of jobs
$15.73 is the 25th percentile. Wages below this are outliers.
$15.20 - $17.11
11% of jobs
$17.11 - $19.02
13% of jobs
The median wage is $19.88 / hr.
$19.02 - $20.92
11% of jobs
$20.92 - $22.83
12% of jobs
$24.56 is the 75th percentile. Wages above this are outliers.
$22.83 - $24.74
8% of jobs
$24.74 - $26.65
8% of jobs
$26.65 - $28.56
6% of jobs
$28.56 - $30.46
5% of jobs
$30.46 - $32.37
4% of jobs
$11
$21
$32
How much do data annotation tech jobs pay per hour?
As of Jul 17, 2026, the average hourly pay for data annotation tech in Seminole, FL is $21.21, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $25.24 per hour, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive in the Data Annotation Tech position, and why are they important?
To thrive as a Data Annotation Tech, you need keen attention to detail, basic computer literacy, and familiarity with data labeling standards, often supported by a high school diploma or equivalent. Experience with annotation platforms, image or text labeling tools, and basic knowledge of data management systems is highly valuable. Strong organizational skills, patience, and effective communication set top candidates apart in this field. These skills and qualities ensure annotated data is accurate, consistent, and valuable for machine learning or AI projects.
What does a typical day look like for a Data Annotation Tech?
A typical day as a Data Annotation Tech involves reviewing large sets of data—such as images, text, or audio—and accurately labeling or categorizing them using specialized software. You may work independently or as part of a team, following specific project guidelines to ensure data integrity and consistency. Collaboration with project managers or data scientists is common when clarifying ambiguous data points or addressing annotation challenges. Additionally, productivity targets and quality checks are a regular part of the workflow, helping to keep projects on schedule and maintain high standards.
What is a Data Annotation Tech job?
A Data Annotation Tech is responsible for labeling and categorizing data, such as text, images, audio, or video, to train machine learning models. They follow specific guidelines to ensure accuracy and consistency in annotations, which helps improve the performance of AI systems. This role often involves repetitive tasks, attention to detail, and familiarity with various annotation tools. Data annotation is crucial for AI development in industries like healthcare, finance, and autonomous driving.
What job categories do people searching Data Annotation Tech jobs in Seminole, FL look for? The top searched job categories for Data Annotation Tech jobs in Seminole, FL are:
What cities near Seminole, FL are hiring for Data Annotation Tech jobs? Cities near Seminole, FL with the most Data Annotation Tech job openings:
Full-time
Re-posted 11 days ago
Job description
Job Summary:
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Lead Data Engineer within the Commercial & Investment Bank, you will design and maintain data pipelines for processing large volumes of scanned document images, ensuring data quality and compliance with regulatory standards.
Responsibilities:
• Design, build, and maintain scalable, high-performance data pipelines and infrastructure to support ingestion, processing, and storage of large volumes of scanned document images across enterprise-wide workflows
• Architect end-to-end data solutions on AWS cloud services to enable seamless flow of scanned images from source systems through OCR processing, model inference, and downstream data extraction and categorization pipelines
• Develop robust image preprocessing and OCR integration pipelines that handle TIF/PNG format conversion, normalization, resolution enhancement, noise reduction, and batching to prepare scanned documents for downstream computer vision and OCR models
• Build and optimize data pipelines that integrate OCR engine outputs, extracting structured text and metadata from scanned images 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-native stores (S3, EFS) to efficiently catalog, index, and retrieve extracted text, classification labels, and metadata from processed document images
• Drive the adoption of containerized deployment strategies using AWS EKS (Elastic Kubernetes Service) to deploy and scale image processing microservices, OCR engines, and data pipeline components with high availability and fault tolerance
• Collaborate closely with data scientists and ML engineers to ensure training datasets for different models, and other computer vision models are properly curated, versioned, labeled, and accessible through well-structured data pipelines
• Evaluate and integrate emerging data technologies and tools to continuously improve pipeline throughput, reduce processing latency for high-volume document scanning workloads, and optimize cost efficiency
• Establish and enforce data quality, lineage, governance, and security frameworks to ensure traceability and integrity of extracted data from scanned documents throughout the entire processing lifecycle
• Partner with security and compliance teams to ensure that scanned document data, extracted PII/PHI, and sensitive content are handled in accordance with 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 building production-grade image and document processing pipelines
Qualifications:
Required:
• Formal training or certification on Data Engineering concepts and 5+ years applied experience
• Strong proficiency in Java, Groovy, and Python for building data pipelines, image preprocessing workflows, automation scripts, and backend data services
• Hands-on experience with image file handling, particularly TIF/PNG format processing, multi-page document splitting, format conversion, and integration with OCR and computer vision pipelines
• Deep hands-on experience with AWS cloud services including S3 (for image storage), Lambda, Step Functions, and CloudWatch for building and monitoring scalable data workflows
• Expertise in AWS EKS (Elastic Kubernetes Service) 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 storing and querying large volumes of extracted document data and classification results
• Familiarity with OCR technologies and the ability to build data pipelines that consume and 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 frameworks, metadata management, and data cataloging, particularly in the context of document-centric and image-heavy data ecosystems
• Excellent leadership, communication, and stakeholder management skills with the ability to drive technical decisions across cross-functional teams
Preferred:
• Domain expertise in the healthcare industry
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Lead Data Engineer within the Commercial & Investment Bank, you will design and maintain data pipelines for processing large volumes of scanned document images, ensuring data quality and compliance with regulatory standards.
Responsibilities:
• Design, build, and maintain scalable, high-performance data pipelines and infrastructure to support ingestion, processing, and storage of large volumes of scanned document images across enterprise-wide workflows
• Architect end-to-end data solutions on AWS cloud services to enable seamless flow of scanned images from source systems through OCR processing, model inference, and downstream data extraction and categorization pipelines
• Develop robust image preprocessing and OCR integration pipelines that handle TIF/PNG format conversion, normalization, resolution enhancement, noise reduction, and batching to prepare scanned documents for downstream computer vision and OCR models
• Build and optimize data pipelines that integrate OCR engine outputs, extracting structured text and metadata from scanned images 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-native stores (S3, EFS) to efficiently catalog, index, and retrieve extracted text, classification labels, and metadata from processed document images
• Drive the adoption of containerized deployment strategies using AWS EKS (Elastic Kubernetes Service) to deploy and scale image processing microservices, OCR engines, and data pipeline components with high availability and fault tolerance
• Collaborate closely with data scientists and ML engineers to ensure training datasets for different models, and other computer vision models are properly curated, versioned, labeled, and accessible through well-structured data pipelines
• Evaluate and integrate emerging data technologies and tools to continuously improve pipeline throughput, reduce processing latency for high-volume document scanning workloads, and optimize cost efficiency
• Establish and enforce data quality, lineage, governance, and security frameworks to ensure traceability and integrity of extracted data from scanned documents throughout the entire processing lifecycle
• Partner with security and compliance teams to ensure that scanned document data, extracted PII/PHI, and sensitive content are handled in accordance with 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 building production-grade image and document processing pipelines
Qualifications:
Required:
• Formal training or certification on Data Engineering concepts and 5+ years applied experience
• Strong proficiency in Java, Groovy, and Python for building data pipelines, image preprocessing workflows, automation scripts, and backend data services
• Hands-on experience with image file handling, particularly TIF/PNG format processing, multi-page document splitting, format conversion, and integration with OCR and computer vision pipelines
• Deep hands-on experience with AWS cloud services including S3 (for image storage), Lambda, Step Functions, and CloudWatch for building and monitoring scalable data workflows
• Expertise in AWS EKS (Elastic Kubernetes Service) 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 storing and querying large volumes of extracted document data and classification results
• Familiarity with OCR technologies and the ability to build data pipelines that consume and 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 frameworks, metadata management, and data cataloging, particularly in the context of document-centric and image-heavy data ecosystems
• Excellent leadership, communication, and stakeholder management skills with the ability to drive technical decisions across cross-functional teams
Preferred:
• Domain expertise in the healthcare industry
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.