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Temporary Ai Data Collection Jobs in Delaware (NOW HIRING)

JPMorgan Chase & Co.

$150 - $190/hr

... data collection, storage, access, and analytics solutions that are secure, stable, and scalable ... Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work ...

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CardWorks

Director, Data Science

Wilmington, DE · On-site

Design and implement AI-powered market intelligence and competitive research solutions that automate the collection, synthesis, and interpretation of market data. * Utilize Large Language Models ...

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Temporary Ai Data Collection information

What is temporary AI data collection?

Temporary AI data collection is a short-term job where individuals gather, label, or organize data used to train artificial intelligence systems. This can involve tasks such as taking photos, recording audio, transcribing text, or annotating images and videos according to specific guidelines. The work is often project-based and helps improve the accuracy and performance of AI models. People in these roles play a vital part in ensuring that AI systems learn from diverse, well-organized, and correctly labeled data.

What does a typical day look like for someone in a temporary AI data collection role?

In a Temporary AI Data Collection position, your daily tasks often involve gathering, labeling, and organizing large sets of data—such as images, text, or audio—that will be used to train machine learning models. You may work independently or as part of a team, following specific guidelines to ensure data accuracy and consistency. Attention to detail is crucial, as even small errors can impact the quality of AI systems. Collaboration with project managers or data scientists is common, especially when clarifying data requirements or resolving ambiguities. The work environment is typically fast-paced, with clear deadlines and performance metrics to meet.

What are the key skills and qualifications needed to thrive as a temporary AI data collection specialist, and why are they important?

To thrive as a Temporary AI Data Collection Specialist, you need attention to detail, basic data entry skills, and the ability to follow structured guidelines, often with a high school diploma or equivalent. Familiarity with data annotation platforms, spreadsheets, and cloud-based collaboration tools is typically required. Reliability, adaptability, and strong communication skills help ensure accurate data collection and effective teamwork. These skills are important for producing high-quality datasets that improve AI models and support project goals.

What is the difference between Temporary Ai Data Collection vs Data Annotator?

AspectTemporary Ai Data CollectionData Annotator
CredentialsBasic computer skills, sometimes familiarity with data collection toolsAttention to detail, basic technical skills, sometimes training in annotation tools
Work EnvironmentRemote or on-site, often flexible hoursRemote or on-site, focused on labeling data
Industry UsageUsed in AI training data gathering, machine learning projectsUsed in preparing datasets for AI models, image/video/text annotation

Temporary Ai Data Collection involves gathering raw data for AI training, often requiring data sourcing skills. Data Annotator focuses on labeling and annotating data to make it usable for machine learning. Both roles are essential in AI development but differ in their specific tasks and skill requirements.

What are the most commonly searched types of Ai Data Collection jobs in Delaware?

The most popular types of Ai Data Collection jobs in Delaware are:

What are popular job titles related to Temporary Ai Data Collection jobs in Delaware?

For Temporary Ai Data Collection jobs in Delaware, the most frequently searched job titles are:

What job categories do people searching Temporary Ai Data Collection jobs in Delaware look for?

The top searched job categories for Temporary Ai Data Collection jobs in Delaware are:

What cities in Delaware are hiring for Temporary Ai Data Collection jobs?

Cities in Delaware with the most Temporary Ai Data Collection job openings:

Temporary Ai Data Collection jobs near you

AWS Lead Software Engineer - Python, Big Data

JPMorgan Chase & Co.

On-site

$150 - $190/hr

Other

Posted 2 days ago

New

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 496 frontline employees who took The Breakroom Quiz

71st of 171 rated banks

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer-Python, AWS, Big Data at JPMorgan Chase within the Corporate Technology-Digital Workflows team, you will be a seasoned member of an agile team, tasked with designing and delivering reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. Your responsibilities will include developing, testing, and maintaining essential data pipelines and architectures across diverse technical areas, supporting various business functions to achieve the firm's business objectives.

Job responsibilities
  • Builds data processing pipeline and deploys applications in production
  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Develops secure high-quality production code, and reviews and debugs code written by others
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
    Leads evaluation sessions with external vendors and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
  • Adds to team culture of diversity, opportunity, inclusion, and respect
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required qualifications, capabilities, and skills
  • Formal training or certification on Software Engineering concepts and 5+ years applied experience
  • Experience on Bigdata, Data platforms and Data Engineering concepts
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • End to end understanding and hands on experience with Developing Data Pipeline Processes
  • Programming experience in Python, FAST API, Spark
  • Technically competent in various AWS Services – Lambda, Glue, Step Function, ECS, AWS Infrastructure provisioning through Terraform
  • Demonstrated experience leading the effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting), with a strong understanding of responsible AI use in engineering workflows—including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security standards. Skilled at setting team expectations for validating AI outputs and coaching engineers on safe, compliant adoption within delivery practices.
  • Ability to tackle design and functionality problems independently with little to no oversight
  • Training in Generative AI solutions like NLP & LLM Models
  • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
  • Training and Work experience in implementation of Generative AI solutions like NLP solutions involving – LLM Models, RAG, Prompt Engineering, AWS Bedrock
  • Previous experience with Snowflake
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