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Ai Data Annotation Remote Jobs in Atlanta, GA (NOW HIRING)

Initio Capital

Growth Strategy Analyst

Atlanta, GA · Remote

... AI/data, business operations, corporate development, private markets, or entrepreneurship. COMPENSATION Compensation varies by project and experience. Many roles are flexible, remote, and project ...

Initio Capital

M&A Research Analyst

Atlanta, GA · Remote

... AI/data, business operations, corporate development, private markets, or entrepreneurship. COMPENSATION Compensation varies by project and experience. Many roles are flexible, remote, and project ...

Initio Capital

FinTech Research Analyst

Atlanta, GA · Remote

... AI/data, business operations, corporate development, private markets, or entrepreneurship. COMPENSATION Compensation varies by project and experience. Many roles are flexible, remote, and project ...

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Ai Data Annotation Remote information

What is an AI data annotation remote?

An AI Data Annotation Remote job involves labeling, tagging, or categorizing data used to train artificial intelligence models. Annotators work with text, images, audio, or video to ensure machine learning algorithms receive accurate and high-quality input. This role is performed remotely, allowing flexibility in work location and schedule. Attention to detail, consistency, and familiarity with annotation tools are essential skills for this job.

What does a typical day look like for someone working remotely in AI data annotation?

A typical day for a remote AI Data Annotation worker involves reviewing and labeling various types of data such as images, text, or audio according to specific guidelines provided by the employer or project lead. You may use specialized annotation software and work through batches of data while following quality standards and deadlines. Periodic team check-ins or virtual meetings help clarify instructions, address questions, and monitor progress. While most of the work is independent, communication with supervisors or quality assurance teams is important to ensure that data labeling is consistent and accurate.

What are the key skills and qualifications needed to thrive in the AI data annotation remote position, and why are they important?

To thrive as an AI Data Annotation Remote worker, you need strong attention to detail, familiarity with data labeling processes, and a basic understanding of machine learning concepts, often supported by a high school diploma or relevant experience. Familiarity with data annotation platforms such as Labelbox, Supervisely, or AWS SageMaker Ground Truth is typically required, and certifications in data annotation or AI may be advantageous. Strong time management, the ability to work independently, and clear communication skills are valuable in this remote role. These abilities ensure accurate and efficient data labeling, which is critical for training reliable AI models.

What are the most commonly searched types of Ai Data Annotation jobs in Atlanta, GA? The most popular types of Ai Data Annotation jobs in Atlanta, GA are:
What are popular job titles related to Ai Data Annotation Remote jobs in Atlanta, GA? For Ai Data Annotation Remote jobs in Atlanta, GA, the most frequently searched job titles are:
What job categories do people searching Ai Data Annotation Remote jobs in Atlanta, GA look for? The top searched job categories for Ai Data Annotation Remote jobs in Atlanta, GA are:
What cities near Atlanta, GA are hiring for Ai Data Annotation Remote jobs? Cities near Atlanta, GA with the most Ai Data Annotation Remote job openings:
Ai Data Annotation Remote jobs near you
Infographic showing various Ai Data Annotation Remote job openings in Atlanta, GA as of August 2026, with employment types broken down into 53% Full Time, 18% Part Time, 4% Temporary, and 25% Contract. Highlights an 100% Remote job distribution.

Enterprise AI Engineer (GCP)

INFT Solutions Inc

Atlanta, GA • On-site, Remote

Contractor

Re-posted 6 days ago

Job description

Job Description: Enterprise AI Engineer (GCP)
Location: Remote / Hybrid Focus: Agentic AI, Data Intelligence, and Enterprise Scale
Role Overview
We are looking for a Principal Enterprise AI Engineer to architect and deliver high-impact AI
solutions within the Google Cloud ecosystem. This role is designed for a technical leader who
can bridge the gap between complex data landscapes and autonomous AI systems. You will lead
the development of Agentic AI frameworks and Data Intelligence platforms that drive
significant digital transformation for global enterprise clients.
Core Responsibilities
 Architect Agentic Systems: Design and deploy multi-agent orchestration frameworks
using Vertex AI Agent Builder, LangGraph, or CrewAI to automate complex, multi-step
business workflows.
 Master RAG Architectures: Build and optimize high-performance Retrieval-
Augmented Generation (RAG) systems, ensuring LLMs are grounded in enterprise data
across BigQuery and Databricks.
 Model Strategy & Optimization: Select and fine-tune models within the Gemini 1.5
family, balancing high-reasoning capabilities (Pro) with high-speed efficiency (Flash) for
production-grade latency.
 Legacy Transformation: Lead the strategic migration of legacy analytics logic (e.g.,
SAS environments) into modern, AI-powered cloud architectures.
 GTM Collaboration: Work closely with Go-To-Market (GTM) leadership to translate
technical AI roadmaps into measurable business value for C-suite stakeholders.
Required Skill Requirements
1. Agentic AI & Orchestration
 Framework Mastery: Expert implementation of LangChain, LangGraph, or
LlamaIndex for stateful, autonomous agent development.
 Advanced Prompting: Proficiency in Chain-of-Thought (CoT), ReAct patterns, and
system instruction optimization to ensure reliable model output.
 Function Calling: Experience building custom tools that allow LLMs to interact
securely with enterprise APIs and SQL databases.
2. Data Intelligence & Engineering
 Hybrid Data Ecosystems: Deep experience integrating Google Cloud AI services with
Databricks (Delta Lake) for unified data intelligence.
 Vector Engineering: Proficiency with Vertex AI Vector Search (formerly Matching
Engine) and embedding strategies for large-scale semantic search.
 Data Flow: Skill in building scalable pipelines using Dataflow or Spark to process
unstructured data for AI readiness.
3. LLMOps & Production Engineering
 Evaluation Frameworks: Ability to build automated "LLM-as-a-judge" evaluation
pipelines to track accuracy, faithfulness, and hallucination rates.
 Cloud Infrastructure: Mastery of the Vertex AI suite (Studio, Model Garden, Pipelines)
and Infrastructure as Code (Terraform).
 Programming: Expert-level Python (FastAPI, Pydantic) and advanced SQL.
4. Strategic Governance
 Responsible AI: Implementation of safety filters, PII redaction, and ethical AI
monitoring.
 Business Translation: Ability to convert technical metrics (latency, token costs) into
business KPIs (ROI, process efficiency).
Qualifications
 Experience: 8+ years in Software Engineering or Data Science, with at least 3+ years
focused on production-grade AI/ML.
 Education: B.S./M.S. in Computer Science, AI, or a related quantitative field.
 Certifications: Google Professional Machine Learning Engineer or Professional Cloud
Architect (preferred).
Technology Stack
 AI/ML: Vertex AI, Gemini 1.5 Pro/Flash, PyTorch.
 Data: BigQuery, Databricks, Vertex Vector Search.
 Orchestration: LangGraph, Vertex AI Agent Builder.
 DevOps: GitHub Actions, Terraform, Vertex AI Pipelines.