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Generative Ai Developer Jobs in Louisiana (NOW HIRING)

... generative AI techniques, including prompt engineering, LLM evaluation, and fine-tuning, to develop production-ready applications powered by foundation models - Developing automated evaluation ...

$50/hr

Master's degree in Computer Science , Electrical Engineering, Applied Mathematics, or related fields. * Proven knowledge and expertise in generative AI applications, including deep generative ...

$50/hr

Master's degree in Computer Science , Electrical Engineering, Applied Mathematics, or related fields. * Proven knowledge and expertise in generative AI applications, including deep generative ...

$50/hr

Master's degree in Computer Science , Electrical Engineering, Applied Mathematics, or related fields. * Proven knowledge and expertise in generative AI applications, including deep generative ...

$50/hr

Master's degree in Computer Science , Electrical Engineering, Applied Mathematics, or related fields. * Proven knowledge and expertise in generative AI applications, including deep generative ...

$50/hr

Master's degree in Computer Science , Electrical Engineering, Applied Mathematics, or related fields. * Proven knowledge and expertise in generative AI applications, including deep generative ...

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Generative Ai Developer information

See Louisiana salary details

$16

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$86

How much do generative ai developer jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for generative ai developer in Louisiana is $38.73, according to ZipRecruiter salary data. Most workers in this role earn between $20.14 and $46.88 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 are popular job titles related to Generative Ai Developer jobs in Louisiana?

For Generative Ai Developer jobs in Louisiana, the most frequently searched job titles are:

What job categories do people searching Generative Ai Developer jobs in Louisiana look for?

The top searched job categories for Generative Ai Developer jobs in Louisiana are:

Infographic showing various Generative Ai Developer job openings in Louisiana as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $80,552 per year, or $38.7 per hour.

Principal AI Engineering Architect

The Mutual Group

Iowa, LA โ€ข On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted yesterday


Job description

Department:

Information Technology

Job Description:

As the Principal AI Engineering Architect, you will play a key role in supporting The Mutual Group (TMG), GuideOne Insurance, and future members by defining and guiding the technical architecture for AI-first engineering, secure AI platforms, reusable components, integration patterns, and scalable technical standards across TMG. This is a senior individual contributor role for a deeply technical architect who can translate complex business and technology needs into practical, secure, and reusable AI-enabled solutions.

This role will work across AI-First IT, Applications, Engineering, Data, Infrastructure, Operations, Security, Architecture, and business teams to design AI capabilities that can move from concept to production with the right architecture, controls, integration model, and operational readiness. The Principal AI Engineering Architect will be expected to stay close to the work, review designs, guide engineering teams, solve difficult technical problems, and create patterns that can be reused across multiple initiatives.

The role will have deeper focus on AI-enabled business processes and AI-first future platforms, while also supporting AI adoption across the IT SDLC and IT operations. The successful candidate will bring strong technical judgment, hands-on architecture depth, and the ability to simplify complex AI engineering concepts into standards, blueprints, and implementation guidance that broader teams can adopt.

Work Arrangement:

  • Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in-office schedule. The initial training period may require additional inoffice days.

Accountabilities:

Architecture Strategy & Technical Direction

  • Define architecture patterns and technical standards for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.

  • Translate business and technology use cases into scalable solution architectures, including application design, data flows, integration patterns, model usage, security controls, and operational requirements.

  • Partner with the Sr. Director, AI Platform and Engineering to shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks.

  • Provide hands-on architecture leadership in design reviews, technical decision-making, proof-of-concept evaluation, implementation planning, and production readiness.

  • Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, cloud AI services, enterprise knowledge systems, and secure deployment practices.

AI Platform Architecture & Reusable Engineering Patterns

  • Design reusable platform patterns for model access, retrieval-augmented generation, vector databases, semantic search, embeddings, enterprise knowledge integration, prompt and response handling, and AI observability.

  • Define integration patterns for connecting AI capabilities with enterprise systems, APIs, data platforms, document repositories, workflow tools, service management platforms, and business applications.

  • Create architecture blueprints, technical standards, reusable components, templates, and implementation guidance that improve speed, consistency, quality, and reuse.

  • Guide decisions on build versus buy, platform selection, vendor capabilities, interoperability, scalability, maintainability, and cost effectiveness.

  • Ensure AI platform patterns are designed for secure production use, including reliability, monitoring, access control, auditability, and lifecycle management.

GenAI, Agentic AI & Model Engineering

  • Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.

  • Define technical patterns for Agentic AI, including tool and function calling, workflow orchestration, human-in-the-loop controls, context management, memory patterns, guardrails, monitoring, and safe execution.

  • Establish usage patterns for Model Context Protocol (MCP) or similar approaches for securely connecting AI systems to enterprise tools, data sources, APIs, and workflow actions.

  • Support practices for model selection, experimentation, evaluation, validation, performance monitoring, drift detection, feedback loops, and responsible production deployment.

  • Help engineering teams design AI solutions that are accurate, observable, explainable where appropriate, cost-aware, and aligned with business and risk expectations.

Business Solution Architecture & Enterprise Adoption

  • Partner with business, product, data, and technology teams to design AI-enabled solutions for underwriting, claims, operations, finance, customer service, and other enterprise functions.

  • Translate business needs into practical AI architectures for decision support, workflow automation, document intelligence, knowledge assistance, triage, summarization, and productivity improvement.

  • Help teams evaluate feasibility, data readiness, integration complexity, user experience, human oversight, and operational support requirements.

  • Create reusable patterns that allow similar AI capabilities to be deployed across multiple business processes with less rework.

  • Communicate architecture decisions clearly to technical and non-technical stakeholders, including tradeoffs, risks, dependencies, and implementation options.

Security, Governance & Engineering Quality

  • Partner with Security, Data, Architecture, and AI & Technology Risk Governance teams to embed secure-by-design, privacy-by-design, and responsible AI practices into solution architecture.

  • Define technical controls for identity and access management, sensitive data handling, prompt and response logging, output validation, human oversight, vendor integration, and production readiness.

  • Ensure AI solutions align with enterprise standards for security, privacy, auditability, observability, resilience, compliance, and operational support.

  • Participate in architecture governance, design reviews, technical risk assessments, and production readiness reviews.

  • Promote engineering quality through strong documentation, testability, traceability, performance considerations, and clear support models.

Qualifications:

  • 10+ years of progressive technology experience across software engineering, architecture, platform engineering, cloud, data, integration, automation, AI, or enterprise technology delivery.

  • 5+ years of experience with AI, machine learning, automation, advanced analytics, intelligent platforms, developer productivity tools, or emerging technology capabilities.

  • Strong technical depth in Generative AI patterns, including LLMs, SLMs, embeddings, prompt engineering, RAG, vector databases, semantic search, evaluation frameworks, and enterprise knowledge integration.

  • Experience with Agentic AI patterns, including agents, tool/function calling, orchestration, human-in-the-loop workflows, context management, guardrails, monitoring, and safe deployment.

  • Familiarity with Model Context Protocol (MCP) or similar approaches for securely connecting AI systems to enterprise tools, data sources, APIs, and workflow actions.

  • Strong understanding of modern architecture patterns, including APIs, microservices, event-driven design, cloud-native platforms, data integration, DevSecOps, CI/CD, observability, cybersecurity, identity, and privacy.

  • Proven experience designing production-grade enterprise platforms, reusable architecture patterns, integration frameworks, automation capabilities, or developer productivity solutions.

  • Experience working in regulated environments with security, privacy, risk, compliance, auditability, and operational readiness expectations preferred.

  • Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or related field required. Master's degree preferred.

Leadership Attributes

  • Deeply technical architect who can dive into details while keeping architecture practical, scalable, and business aligned.

  • Strong systems thinker who simplifies complexity into reusable patterns, standards, and clear technical guidance.

  • Hands-on problem solver with strong analytical reasoning and sound judgment.

  • Collaborative partner who builds trust across business, engineering, data, security, infrastructure, operations, and governance teams.

  • Constructive challenger who raises technical standards while helping teams move quickly and responsibly.

  • Strong communicator who can explain complex AI and architecture concepts in clear, actionable terms.

Pay Range:

Anticipated Hiring Range:

  • $170,000 - $210,000annual base salary depending on experience, qualifications, and geographic location

Benefits:

We are proud to offer our full-time regular employees a robust benefits suite that includes:

  • Competitive base salary plus incentive plans for eligible team members

  • 401(K) retirement plan that includes a company match of up to 6% of your eligible salary

  • Free basic life and AD&D, long-term disability and short-term disability insurance

  • Medical, dental and vision plans to meet your unique healthcare needs

  • Wellness incentives

  • Generous time off program that includes personal, holiday and volunteer paid time off

  • Flexible work schedules and hybrid/remote options for eligible positions

  • Educational assistance

Equal Opportunity Employer

The Mutual Groupis an Equal Opportunity Employer. It is our policy to recruit, hire, train and promote individuals in all job classifications without regard to race, color, religion, sex, national origin, age, veteran status, disability, sexual orientation, gender identity or any other characteristic protected by law.

  • Know Your Rights: Workplace Discrimination is Illegal

  • Your Rights Under USERRA

Applicants requiring a reasonable accommodation due to a disability at any stage of the employment application process should contactTalent@themutualgroup.com.

Employment Verification

The Mutual Group participates in theE-Verifyprogram and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. You are protected fromemployment discriminationbased on your citizenship status and national origin.

E-Verify Program Overview

E-Verify Participation Poster

All offers of employment are contingent upon the successful completion of a background check.

#TMG