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Ai Labelling Jobs in Georgia (NOW HIRING)

Insight Global

AI Engineer

Atlanta, GA · On-site +1

... dataset and labeling workflow. • Familiarity with prompt-injection risks and mitigation ... AI -- PDF parsing, table extraction, or OCR pipelines. • Has contributed to or built an ...

RELX Group plc

AI Data Analyst

Alpharetta, GA · On-site

Support basic semantic labeling or categorization efforts that improve AI retrieval and reasoning (in coordination with engineering teams). * Promote good content hygiene practices (clear structure ...

RELX

AI Data Analyst

Alpharetta, GA · On-site

Support basic semantic labeling or categorization efforts that improve AI retrieval and reasoning (in coordination with engineering teams). * Promote good content hygiene practices (clear structure ...

US Bank

AI Red Team Lead Engineer

Atlanta, GA

$98K - $129K/yr

Data ingestion, labeling, and governance controls * Design and execute AI-specific threat emulation aligned to real-world adversaries, misuse scenarios, and emerging attack techniques (e.g., prompt ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA

$117K - $155K/yr

As a Full Stack AI Platform Engineer here at Honeywell, you will design, build, and scale AI ... Design automated workflows to collect, label, and manage datasets, ensuring high-quality data is ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA · On-site

$117K - $155K/yr

As a Full Stack AI Platform Engineer here at Honeywell, you will design, build, and scale AI ... Design automated workflows to collect, label, and manage datasets, ensuring high-quality data is ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA

$117K - $155K/yr

As a Full Stack AI Platform Engineer here at Honeywell, you will design, build, and scale AI ... Design automated workflows to collect, label, and manage datasets, ensuring high-quality data is ...

LAVA SCS LLC

Outbound Lead

Garden City, GA · On-site

$22.50/hr

Ensure proper packaging and labeling of outgoing shipments to comply with shipping regulations ... By unifying transportation, assets, and information through AI-enabled technology, UNIS helps ...

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Ai Labelling information

What are some typical challenges faced in AI labelling roles and how can they be managed?

One common challenge in AI Labelling roles is maintaining accuracy and consistency when labeling large volumes of data according to detailed guidelines, which can become repetitive or mentally taxing. Managing these challenges often involves taking regular breaks, double-checking work, and staying up-to-date with any updates to annotation standards provided by the team. Collaborating with supervisors and peers to clarify uncertainties and seek feedback also helps ensure high-quality output. Over time, professionals in this role often develop efficient workflows and a keen eye for detail, opening doors to advancement into quality assurance or project coordination positions within the data annotation field.

What is an AI labelling?

An AI labelling job involves annotating data—such as images, text, audio, or video—to help train machine learning models. This process includes tasks like tagging objects in images, transcribing speech, or categorizing text. The labelled data is crucial for AI systems to learn and make accurate predictions. These jobs are commonly found in industries like tech, healthcare, and autonomous driving. Attention to detail and consistency are key skills for this role.

What are the key skills and qualifications needed to thrive in AI labelling?

To thrive in an AI Labelling role, you need attention to detail, basic data analysis skills, and the ability to follow complex guidelines, with many roles requiring at least a high school diploma or equivalent. Familiarity with data annotation tools, image or text labeling platforms, and sometimes basic scripting or database systems is beneficial. Strong communication, time management, and the ability to work both independently and as part of a team are valuable soft skills. These competencies ensure the consistent and accurate labeling of data, which is critical for training high-quality AI and machine learning models.

What are popular job titles related to Ai Labelling jobs in Georgia?

For Ai Labelling jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Ai Labelling jobs in Georgia look for?

The top searched job categories for Ai Labelling jobs in Georgia are:

What cities in Georgia are hiring for Ai Labelling jobs?

Cities in Georgia with the most Ai Labelling job openings:

Ai Labelling jobs near you
Infographic showing various Ai Labelling job openings in Georgia as of August 2026, with employment types broken down into 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution.

AI Engineer

Insight Global

Atlanta, GA • On-site, Remote

Full-time

Re-posted 13 days ago

Job description

Overview

You will work across the AI layer of the platform — contributing to retrieval pipelines, agent workflows, and model evaluation. The work is hands-on and empirical: you run experiments, measure results, and iterate. You will work closely with the Senior AI Engineer and broader engineering team, taking increasing ownership as you develop depth across the platform's AI systems.


Responsibilities

• Contribute to the hybrid retrieval pipeline — implementing and tuning retrieval components, running experiments to improve quality, and validating results on real evaluations.
• Build and maintain components of the agent orchestration layer — tool integrations, prompt management, and supporting human-in-the-loop workflows.
• Instrument AI and agent systems for observability — tracing model and tool calls, capturing token and latency telemetry, and supporting failure analysis.
• Build and maintain evaluation datasets and test suites across retrieval and agent workflows, contributing to CI-level quality gates.
• Support document AI capabilities — working with parsing, extraction, and OCR pipelines as the platform serves new client engagements.
• Implement per-tenant isolation checks across retrieval and agent layers, helping ensure no cross-tenant data leakage occurs.
• Contribute to model behaviour evaluation — running prompt injection and adversarial tests as part of ongoing eval work.


Qualifications

Required qualifications
• 3+ years of software engineering experience, with at least 1 year building LLM-powered systems — RAG pipelines, agent workflows, or fine-tuning — in a production or nearproduction setting.
• Practical experience in at least one of: retrieval-augmented generation (RAG) and reranking; agent orchestration with LangGraph or comparable; or LLM fine-tuning.
• Proficient in Python and comfortable working with async code, data pipelines, and REST APIs.
• Exposure to evaluation methodology for LLM systems — has contributed to or authored an eval dataset or test suite.
• Familiarity with agent or LLM observability tooling — tracing, logging, or monitoring of model and tool calls.
• Working knowledge of modern LLM and information-retrieval concepts; can discuss trade-offs between approaches with evidence.

Preferred qualifications
• Experience with knowledge graphs or property-graph query languages.
• Exposure to document AI — PDF parsing, table extraction, or OCR pipelines.
• Has contributed to or built an evaluation dataset and labeling workflow.
• Familiarity with prompt-injection risks and mitigation strategies in agent and retrieval pipelines.
• Open-source contributions to LangGraph, sentence-transformers, or comparable projects.

Qualifications:

Required qualifications
• 3+ years of software engineering experience, with at least 1 year building LLM-powered systems — RAG pipelines, agent workflows, or fine-tuning — in a production or nearproduction setting.
• Practical experience in at least one of: retrieval-augmented generation (RAG) and reranking; agent orchestration with LangGraph or comparable; or LLM fine-tuning.
• Proficient in Python and comfortable working with async code, data pipelines, and REST APIs.
• Exposure to evaluation methodology for LLM systems — has contributed to or authored an eval dataset or test suite.
• Familiarity with agent or LLM observability tooling — tracing, logging, or monitoring of model and tool calls.
• Working knowledge of modern LLM and information-retrieval concepts; can discuss trade-offs between approaches with evidence.

Preferred qualifications
• Experience with knowledge graphs or property-graph query languages.
• Exposure to document AI — PDF parsing, table extraction, or OCR pipelines.
• Has contributed to or built an evaluation dataset and labeling workflow.
• Familiarity with prompt-injection risks and mitigation strategies in agent and retrieval pipelines.
• Open-source contributions to LangGraph, sentence-transformers, or comparable projects.

Education:UNAVAILABLEEmployment Type: FULL_TIME

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