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Ai Labelling Jobs in Georgia (NOW HIRING)

Machine Learning AI Engineer

Atlanta, GA · Hybrid

$110K - $132K/yr

... labeling strategies, and model evaluation with strong rigor • Implement model interpretability, bias assessment, and responsible AI practices Production Engineering & MLOps • Build production ...

New

AI Red Team Lead Engineer

Atlanta, GA · On-site

$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 ...

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 ...

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 ...

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 ...

Perform feature engineering, dataset creation, labeling strategies, and model evaluation with ... Implement techniques for model interpretability, bias assessment, and responsible AI where ...

New

Senior Software Engineer (Gen AI)

Atlanta, GA · On-site

$117K - $155K/yr

As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we're ... labeling, model development, validation, deployment, and ongoing monitoring. * An ability to ...

... labeling strategies, and model evaluation with strong scientific rigor. • Implement techniques for model interpretability, bias assessment, and responsible AI where applicable. • Build production ...

Familiarity with Copilot/AI data risk considerations and emerging security controls. * Basic understanding of data classification/labeling, DLP policies and rules, and regulatory concepts (e.g., GDPR ...

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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 job?

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 the Ai Labelling position, and why are they important?

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:
Machine Learning AI Engineer

Machine Learning AI Engineer

RIT Solutions

Atlanta, GA • Hybrid

$110K - $132K/yr

Other

This job post has expired today. Applications are no longer accepted.


Job description

Machine Learning AI Engineer

Location: Hybrid in Atlanta, GA (3 days onsite 2 days remote) They want someone who can come in and hit the ground running from day 1. Full details are below. Top requirements are Python, Fast API/Flask, and ML Pipeline development. Locals only for now.

Top 3 Skills:

• Production-grade machine learning system development • Strong Python and ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost) • Data engineering and ML pipeline development

Detailed Responsibilities:

Model Development & Applied AI

• Partner with stakeholders to frame business problems as ML/AI use cases, define success metrics, and identify required data • Build and iterate on models (classification, regression, ranking, clustering, anomaly detection, NLP) • Perform feature engineering, dataset creation, labeling strategies, and model evaluation with strong rigor • Implement model interpretability, bias assessment, and responsible AI practices

Production Engineering & MLOps

• Build production-grade ML services and pipelines (batch and real-time) • Deploy models using CI/CD and infrastructure-as-code practices • Monitor data drift, model drift, latency, throughput, cost, and model quality • Maintain versioning of datasets, features, models, and experiments

Data & Platform Collaboration

• Collaborate with data engineering to build and maintain robust pipelines • Work with software engineers to integrate ML models via APIs, event streams, or workflows • Document architecture, runbooks, and model artifacts; participate in technical reviews

Required Skills & Technologies:

• 3–5+ years of experience deploying ML systems to production • Strong Python programming and ML libraries (scikit-learn, PyTorch, TensorFlow, XGBoost) • Data processing experience with Pandas, NumPy, SQL, and Spark (as applicable) • Experience building ML services (FastAPI or Flask), containerization (Docker), and CI/CD • Experience developing APIs and integrating with third-party APIs (4+ years) • Strong SQL skills (SQL Server or Oracle) • Familiarity with scripting (Python, Bash, PowerShell) • Version control experience (Git, GitHub, GitLab) • Understanding of DevOps and CI/CD best practices