AI Engineer Visa Sponsorship Jobs in Florida
Florida's AI engineering market is expanding beyond Miami's tech corridor, with major employers like Chewy, Ultimate Kronos Group (UKG), and AdventHealth actively hiring in Tampa, Orlando, and Fort Lauderdale. The state's growing fintech, healthtech, and defense contracting sectors create steady demand for AI engineers, and Florida's lack of state income tax makes it an attractive destination for internationally sponsored talent.
Find AI Engineer JobsOverview
Showing 5 of 101+ AI Engineer Jobs in Florida with Visa Sponsorship
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all 101+ AI Engineer Jobs in Florida with Visa Sponsorship
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI Engineer Jobs in Florida with Visa Sponsorship.
Get Access To All JobsSenior AI Engineer – ML & Generative AI
Role Overview
We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems. This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows—not research prototypes. You will work in small, high-impact delivery teams (2–3 engineers per initiative) and spend the majority of your time (~70–75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.
Key Responsibilities
AI Solution Design & Problem Solving
- Partner with business and product stakeholders to translate real-world problems into practical AI solutions.
- Determine when to apply:
- Traditional ML approaches (classification, regression, clustering, recommendation systems)
- LLM / GenAI approaches, including agentic workflows
- Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity.
- Design iterative AI workflows and propose alternative solution approaches where applicable.
Hands-on Engineering & Delivery (70–75%)
- Build and own end-to-end AI systems, including:
- Data ingestion and processing pipelines
- Feature engineering and prompt construction
- ML and LLM integration and orchestration
- API-based AI services for downstream consumption
- Deploy and harden production AI systems with:
- Error handling and fallback mechanisms
- Guardrails, safety controls, and exception handling
- Observability (logging, metrics, tracing, dashboards)
- Ensure production readiness through:
- Performance tuning and latency optimization
- Cost management and optimization strategies
- Scalability and reliability planning
- Implement AI system controls such as:
- Input validation and prompt injection mitigation
- Configurable policies and kill switches
- Transition PoCs into production-grade systems through refactoring, testing, and system hardening.
ML & Generative AI Expertise
- Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques.
- Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations.
- Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models).
- Design and implement RAG (Retrieval-Augmented Generation) architectures.
- Apply prompt engineering, evaluation techniques, and iterative optimization.
- Build and evolve tool-based and agentic workflows, including multi-agent systems.
- Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems).
Collaboration & Technical Leadership (25–30%)
- Act as a senior technical contributor within small delivery teams.
- Debug complex AI system behavior and production issues beyond prompt-level tuning.
- Contribute to architectural and design decisions alongside architects and platform teams.
- Collaborate closely with:
- Product managers and business stakeholders
- Platform, cloud, and infrastructure teams
- Uphold strong software engineering practices and delivery discipline.
Required Skills & Experience
Software & Systems Engineering
- 10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent.
- Strong backend development skills (Python, Java, Node.js, or similar languages).
- Experience designing and building REST or gRPC-based services.
- Solid understanding of distributed system design.
- Containerization and orchestration experience (Docker, Kubernetes).
AI / ML
- Hands-on experience across traditional ML and modern GenAI systems.
- Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents.
- Experience building or deploying:
- ML-driven production systems
- LLM-based applications
- Ability to select ML vs. LLM-driven approaches based on business and operational constraints.
Cloud & DevOps
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
- Experience with CI/CD pipelines and deployment automation.
- Understanding of model, code, and configuration versioning best practices.
Observability & Production Readiness
- Experience implementing logging, monitoring, and tracing for production systems.
- Familiarity with system resilience patterns such as:
- Rate limiting
- Failover strategies
- Kill-switch mechanisms
Problem Solving & Mindset
- Strong ability to solve ambiguous, real-world engineering problems.
- Comfortable working in fast-moving, iterative environments.
- Ownership mindset with a bias toward practical, scalable solutions.
Communication & Collaboration
- Experience working in cross-functional teams.
- Ability to clearly articulate technical and business trade-offs, including:
- LLM vs traditional ML
- Build vs buy decisions
- Speed vs robustness
Good to Have
- Experience with enterprise AI platforms or internal AI frameworks.
- Prior production experience with:
- Agentic architectures
- Multi-agent systems
- RAG-based systems at scale
- Exposure to AI governance, safety, and compliance considerations.
- Experience mentoring junior engineers or owning technical modules.
- Hands-on experience optimizing performance and cost for AI workloads.
Senior AI Engineer – ML & Generative AI
Role Overview
We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems. This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows—not research prototypes. You will work in small, high-impact delivery teams (2–3 engineers per initiative) and spend the majority of your time (~70–75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.
Key Responsibilities
AI Solution Design & Problem Solving
- Partner with business and product stakeholders to translate real-world problems into practical AI solutions.
- Determine when to apply:
- Traditional ML approaches (classification, regression, clustering, recommendation systems)
- LLM / GenAI approaches, including agentic workflows
- Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity.
- Design iterative AI workflows and propose alternative solution approaches where applicable.
Hands-on Engineering & Delivery (70–75%)
- Build and own end-to-end AI systems, including:
- Data ingestion and processing pipelines
- Feature engineering and prompt construction
- ML and LLM integration and orchestration
- API-based AI services for downstream consumption
- Deploy and harden production AI systems with:
- Error handling and fallback mechanisms
- Guardrails, safety controls, and exception handling
- Observability (logging, metrics, tracing, dashboards)
- Ensure production readiness through:
- Performance tuning and latency optimization
- Cost management and optimization strategies
- Scalability and reliability planning
- Implement AI system controls such as:
- Input validation and prompt injection mitigation
- Configurable policies and kill switches
- Transition PoCs into production-grade systems through refactoring, testing, and system hardening.
ML & Generative AI Expertise
- Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques.
- Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations.
- Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models).
- Design and implement RAG (Retrieval-Augmented Generation) architectures.
- Apply prompt engineering, evaluation techniques, and iterative optimization.
- Build and evolve tool-based and agentic workflows, including multi-agent systems.
- Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems).
Collaboration & Technical Leadership (25–30%)
- Act as a senior technical contributor within small delivery teams.
- Debug complex AI system behavior and production issues beyond prompt-level tuning.
- Contribute to architectural and design decisions alongside architects and platform teams.
- Collaborate closely with:
- Product managers and business stakeholders
- Platform, cloud, and infrastructure teams
- Uphold strong software engineering practices and delivery discipline.
Required Skills & Experience
Software & Systems Engineering
- 10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent.
- Strong backend development skills (Python, Java, Node.js, or similar languages).
- Experience designing and building REST or gRPC-based services.
- Solid understanding of distributed system design.
- Containerization and orchestration experience (Docker, Kubernetes).
AI / ML
- Hands-on experience across traditional ML and modern GenAI systems.
- Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents.
- Experience building or deploying:
- ML-driven production systems
- LLM-based applications
- Ability to select ML vs. LLM-driven approaches based on business and operational constraints.
Cloud & DevOps
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
- Experience with CI/CD pipelines and deployment automation.
- Understanding of model, code, and configuration versioning best practices.
Observability & Production Readiness
- Experience implementing logging, monitoring, and tracing for production systems.
- Familiarity with system resilience patterns such as:
- Rate limiting
- Failover strategies
- Kill-switch mechanisms
Problem Solving & Mindset
- Strong ability to solve ambiguous, real-world engineering problems.
- Comfortable working in fast-moving, iterative environments.
- Ownership mindset with a bias toward practical, scalable solutions.
Communication & Collaboration
- Experience working in cross-functional teams.
- Ability to clearly articulate technical and business trade-offs, including:
- LLM vs traditional ML
- Build vs buy decisions
- Speed vs robustness
Good to Have
- Experience with enterprise AI platforms or internal AI frameworks.
- Prior production experience with:
- Agentic architectures
- Multi-agent systems
- RAG-based systems at scale
- Exposure to AI governance, safety, and compliance considerations.
- Experience mentoring junior engineers or owning technical modules.
- Hands-on experience optimizing performance and cost for AI workloads.
AI Engineer Job Roles in Florida
See all 101+ AI Engineer Jobs in Florida
Sign up for free to filter by visa type, set job alerts, and find employers with verified sponsorship history.
Search AI Engineer Jobs in FloridaAI Engineer Jobs in Florida: Frequently Asked Questions
Which companies in Florida sponsor visas for AI engineers?
Several Florida-based employers have active H-1B visa sponsorship histories for AI and machine learning roles. These include UKG (Ultimate Kronos Group) in Weston, Chewy in Plantation, Lockheed Martin and Northrop Grumman in Orlando, and health systems like AdventHealth and Tampa General Hospital investing in AI-driven clinical tools. Large consulting firms with Florida offices, including Deloitte and Accenture, also sponsor AI engineering roles regularly.
Which visa types are most common for AI engineer roles in Florida?
The H-1B is the most common visa for AI engineers in Florida, as the role typically qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, data science, or a related field. Candidates from Australia may qualify for the E-3 visa, and Canadian and Mexican nationals can explore TN visa status under the USMCA. Individuals with exceptional research or publication records may also qualify for the O-1A.
How to find ai engineer visa sponsorship jobs in Florida?
Migrate Mate is built specifically for international candidates seeking visa sponsorship jobs, including AI engineer roles in Florida. You can filter by state and job title to surface employers with active sponsorship histories in cities like Miami, Orlando, and Tampa. Because Migrate Mate focuses exclusively on sponsorship-verified listings, it removes the guesswork of cold-applying to companies that don't sponsor.
Which cities in Florida have the most AI engineer visa sponsorship jobs?
Miami leads Florida for AI engineering activity, driven by a fast-growing tech startup scene and proximity to Latin American markets attracting investment. Orlando follows closely, supported by defense contractors, simulation technology firms, and UCF's strong computer science pipeline. Tampa is emerging as a fintech and cybersecurity hub, with companies there increasingly hiring AI talent. Fort Lauderdale and Jacksonville also have pockets of enterprise tech demand.
Are there state-specific factors that affect AI engineer visa sponsorship in Florida?
Florida has no state income tax, which can make total compensation more competitive for sponsored hires compared to states like California or New York. The University of Florida, University of Miami, and UCF produce strong local AI and machine learning talent pipelines, meaning employers are familiar with sponsoring OPT and H-1B candidates. Florida's defense and aerospace sector in the Orlando-Cape Canaveral corridor sometimes involves security clearance requirements, which can complicate sponsorship for some international candidates.
What is the prevailing wage for sponsored ai engineer jobs in Florida?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.