1

Data Annotation For Ai Jobs in Dallas, GA (NOW HIRING)

Senior AI Data Engineer

Atlanta, GA · On-site

$121K - $151K/yr

The base pay range for this role is estimated to be $121,200.00 - $151,500.00 at the time of ... Evolve the Enterprise Data Platform into an AI-native platform by enabling intelligent discovery ...

Lead Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

Knowledge of data security and governance practices for AI systems * Experience working on ... analytics projects with Agile and Scrum Methodologies US PERSON REQUIREMENTS : Due to compliance ...

Lead Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

Knowledge of data security and governance practices for AI systems * Experience working on ... analytics projects with Agile and Scrum Methodologies US PERSON REQUIREMENTS : Due to compliance ...

Support end-to-end workflows for AI and ML use cases, including data preparation, experimentation, deployment, monitoring, and lifecycle management. * Contribute to CI/CD practices for AI-enabled ...

Support end-to-end workflows for AI and ML use cases, including data preparation, experimentation, deployment, monitoring, and lifecycle management. * Contribute to CI/CD practices for AI-enabled ...

Showing results 21-40

Data Annotation For Ai information

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

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

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What are popular job titles related to Data Annotation For Ai jobs in Dallas, GA?

For Data Annotation For Ai jobs in Dallas, GA, the most frequently searched job titles are:

What job categories do people searching Data Annotation For Ai jobs in Dallas, GA look for?

The top searched job categories for Data Annotation For Ai jobs in Dallas, GA are:

What cities near Dallas, GA are hiring for Data Annotation For Ai jobs?

Cities near Dallas, GA with the most Data Annotation For Ai job openings:

Senior AI Data Engineer

Kiongroup

Atlanta, GA • On-site

$121K - $151K/yr

Full-time

Re-posted 3 days ago


Job description

We are seeking a highly experienced Senior AI Data Engineer to help transform our Enterprise Data Platform into an AI-native, intelligent platform where AI agents can discover, understand, and interact with enterprise data and platform capabilities.
This role focuses on building the foundations that enable AI agents and intelligent applications to effectively leverage enterprise data, including context engineering, semantic understanding, metadata intelligence, AI-ready data abstractions, and agent-driven platform capabilities.
This is a hands-on senior role requiring deep expertise in cloud data engineering, AI-enabled data platforms, agentic AI architectures, semantic modeling, metadata and context engineering, and modern software development practices. The ideal candidate combines strong technical execution skills with architectural thinking and the ability to design and deliver scalable AI capabilities that integrate seamlessly with enterprise data platforms and business workflows.We offer:
  • Career Development
  • Competitive Compensation and Benefits
  • Pay Transparency
  • Global Opportunities

Learn More Here:https://www.dematic.com/en-us/about/careers/what-we-offer

Dematic provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

The base pay range for this role is estimated to be $121,200.00 - $151,500.00 at the time of posting. Final compensation will be determined by various factors such as work location, education, experience, knowledge and skills.

Tasks and Qualifications:

This is What You Will do in This Role:

  • Evolve the Enterprise Data Platform into an AI-native platform by enabling intelligent discovery, understanding, and utilization of enterprise data.
  • Design and implement AI-driven capabilities and agents that enhance data platform capabilities, automate complex workflows, and improve how data is discovered, managed, governed, and consumed.
  • Build the data intelligence foundation required for AI systems, including trusted context, business understanding, and reliable access to enterprise data.
  • Design architectures that enable AI systems to reason over enterprise data and safely interact with platform capabilities, APIs, services, and enterprise applications.
  • Develop scalable AI-enabled solutions that integrate with cloud data platforms, distributed systems, and modern software architectures.
  • Establish engineering practices for reliable production AI capabilities, including security, governance, evaluation, monitoring, and operational excellence.
  • Apply strong data engineering and software engineering principles to build scalable, maintainable AI-enabled platform capabilities.
  • Partner with data, AI/ML, architecture, and product teams to identify and deliver high-impact AI capabilities for the enterprise data platform.
  • Mentor engineers and define best practices for AI-enabled data platform development.

What We are Looking For:

  • 8-12+ years of experience in enterprise software engineering, cloud data engineering, distributed systems, or data platform development.
  • Hands-on experience designing and building production AI systems, AI agents, or agentic workflows integrated with enterprise applications, APIs, and platform services.
  • Strong understanding of AI agent architectures, including tool calling, orchestration, context management, memory, evaluation, observability, and production deployment patterns.
  • Experience building AI-ready data platforms with capabilities such as semantic understanding, metadata intelligence, context engineering, and trusted data access.
  • Strong cloud data engineering experience, preferably in GCP, including BigQuery, Pub/Sub, Dataflow/Cloud Run, Composer/Airflow, and modern data platform services.
  • Strong programming skills in Python and experience building scalable software services, APIs, and microservice architectures.
  • Deep understanding of data engineering fundamentals, including data modeling, data contracts, metadata, lineage, governance, data quality, and batch/streaming architectures.
  • Experience integrating AI capabilities with enterprise data platforms and distributed systems.
  • Experience with modern data and cloud-native technologies such as Iceberg, Trino, Kubernetes, and Docker.
  • Experience designing secure, governed, and observable production AI solutions, including evaluation, monitoring, and operational excellence.

What Will Set You Apart:

  • Experience building AI agents that execute real-world enterprise workflows, beyond conversational assistants or prototypes.
  • Experience with AI frameworks and platforms such as Google ADK, Vertex AI, MCP, LangGraph, or similar technologies.
  • Experience applying RAG, embeddings, vector search, semantic layers, or knowledge graphs to enterprise AI solutions.
  • Experience with Data Mesh, domain-driven data architecture, or federated data platforms.
  • Supply chain, logistics, warehouse automation, or industrial domain experience.

Location & Authorization:This is a hybrid role requiring proximity to one of our U.S. offices (Atlanta GA, Grand Rapids MI, Milwaukee WI).Applicants must be authorized to work in the U.S. without the need for current or future sponsorship.

#LI-DP1