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Generative Ai Developer Jobs in Callahan, FL (NOW HIRING)

Citi

Lead Generative AI Engineer, VP

Jacksonville, FL · On-site

$90K - $118K/yr

... the AI Automation Forward Deployed Engineering (FDE) team, part of Consumer Operations Technology. In this senior hands-on role, you will build and operationalize generative AI solutions using ...

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Generative Ai Developer information

See Callahan, FL salary details

$16

$40

$89

How much do generative ai developer jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for generative ai developer in Callahan, FL is $40.17, according to ZipRecruiter salary data. Most workers in this role earn between $20.91 and $48.61 per hour, depending on experience, location, and employer.

What is a generative AI developer?

A Generative AI Developer is a technology professional who specializes in designing, building, and deploying artificial intelligence systems that can create new content, such as text, images, audio, or code. They work with advanced machine learning models, like generative adversarial networks (GANs) or large language models, to enable computers to produce original outputs. These developers often collaborate with data scientists, researchers, and product teams to integrate AI-generated content into software applications and business solutions.

What are the key skills and qualifications needed to thrive as a generative AI developer?

To thrive as a Generative AI Developer, you need strong programming skills (especially in Python), a deep understanding of machine learning concepts, and an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and experience with cloud platforms or model deployment tools are typically required. Creative problem-solving, adaptability, and effective collaboration are standout soft skills in this evolving field. These abilities are crucial to design, implement, and refine generative models that solve real-world problems and drive innovation.

What are some common challenges faced by generative AI developers when deploying models in production environments?

Generative AI Developers often encounter challenges such as ensuring model reliability, managing computational resource requirements, and addressing ethical considerations like data bias or content safety. Deploying generative models at scale requires robust monitoring to detect unexpected outputs or model drift, and collaboration with data engineers and product teams to optimize performance. Staying up-to-date with evolving frameworks and best practices is essential, as production environments demand both technical rigor and adaptability to new AI advancements.

What is the difference between Generative Ai Developer vs Machine Learning Engineer?

AspectGenerative Ai DeveloperMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with deep learning frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI models for content creation, chatbots, and creative applicationsBuilds and deploys ML models for various data-driven solutions across industries
Industry UsageTech, entertainment, marketing, and creative sectorsFinance, healthcare, tech, and e-commerce sectors

While both roles involve AI and machine learning, Generative Ai Developers focus on creating models that generate content, such as images or text, whereas Machine Learning Engineers develop broader ML solutions for diverse applications. The roles often overlap but differ mainly in their specific focus areas and use cases.

How to become a generative AI developer?

To become a generative AI developer, you should have a strong foundation in programming languages like Python, experience with machine learning frameworks such as TensorFlow or PyTorch, and knowledge of neural network architectures like transformers. Gaining expertise in natural language processing and deep learning, along with practical experience through projects or internships, is essential. Certifications in AI or data science can also enhance your qualifications.

What job categories do people searching Generative Ai Developer jobs in Callahan, FL look for?

The top searched job categories for Generative Ai Developer jobs in Callahan, FL are:

What cities near Callahan, FL are hiring for Generative Ai Developer jobs?

Cities near Callahan, FL with the most Generative Ai Developer job openings:

Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in Callahan, FL as of August 2026, with employment types broken down into 77% Full Time, 17% Part Time, and 6% Contract. Highlights an 61% Physical, 4% Hybrid, and 35% Remote job distribution, with an average salary of $83,564 per year, or $40.2 per hour.

Information Technology_USA - USA_Developer

Real Soft, Inc.

Jacksonville, FL • On-site

Contractor

Re-posted 19 days ago

Job description

Please strictly adhere to the following resume naming convention:
ALL CAPS, NO SPACES B/T UNDERSCORES
PTN_US_GBAMSREQID_CandidateBeelineID
i.e. PTN_US_9999999_SKIPJOHNSON0413
: MAX CONFIRMED-/Hr(Max)
MSP Owner: Shilpa Bajpai
Location: Denver, CO (Zip Code-80221)- REMOTE
Duration: 6 months
Requisition ID: 10899783
Role: Senior AI Automation Engineer (Gen AI Developer)
Skills: AI & Gen AI - Products & Tools
Experience Required: 2-4 Years
Role Descriptions:
Generative AI and enterprise agent-based solutions| focusing on the design| development| and deployment of scalable AI applications.
Designing and developing AI Agents and enterprise automation workflows to address business use cases.
Building and integrating MCP (Model Context Protocol) servers| tools| and connectors to enable secure access to enterprise applications| APIs| databases| and external systems.
Developing and maintaining LLM-powered solutions| including prompt engineering| agent orchestration| tool calling| and Retrieval-Augmented Generation (RAG) implementations.
Creating and supporting Python-based microservices and APIs that integrate AI capabilities with enterprise platforms.
Deploying| managing| and troubleshooting applications using Docker and Kubernetes (AKS| EKS| and GKE) in cloud-native environments.
Working across Azure| AWS| and GCP to build secure| scalable| and highly available solutions.

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