Senior AI Software Engineer Visa Sponsorship Jobs in Florida
Florida's senior AI software engineer market is anchored by major tech employers and defense contractors across Miami, Orlando, and Tampa. Companies like Lockheed Martin, Chewy, and Citigroup's Florida operations actively hire for advanced AI roles. The state's growing fintech corridor and university research partnerships create steady demand for senior-level AI talent requiring visa sponsorship.
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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.
Senior AI Software Engineer Job Roles in Florida
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Search Senior AI Software Engineer Jobs in FloridaSenior AI Software Engineer Jobs in Florida: Frequently Asked Questions
Which companies sponsor visas for senior AI software engineers in Florida?
Defense and aerospace contractors such as Lockheed Martin and L3Harris, headquartered or with major Florida operations, regularly sponsor H-1B visas for senior AI engineers. Large technology-forward employers including Chewy, Citigroup's Tampa technology hub, and IBM have consistent sponsorship track records in the state. Healthtech firms in the Orlando corridor and fintech companies in Miami also appear frequently in H-1B disclosure data for senior AI roles.
Which visa types are most common for senior AI software engineer roles in Florida?
The H-1B is the most common visa for senior AI software engineers in Florida, as these roles meet the specialty occupation standard requiring a bachelor's degree or higher in computer science, artificial intelligence, or a related field. Candidates with extraordinary recognition in AI research may qualify for the O-1A. Multinational company employees may also be eligible for an L-1A or L-1B intracompany transfer depending on their employment history.
Which cities in Florida have the most senior AI software engineer sponsorship jobs?
Miami leads Florida for senior AI software engineer sponsorship activity, driven by its fintech and tech startup growth. Tampa follows closely, with Citigroup's large technology operations and a growing health IT sector anchoring demand. Orlando attracts sponsorship through defense contractors, simulation technology firms, and proximity to UCF's AI research programs. Jacksonville has a smaller but active financial services technology market that sponsors senior AI talent.
How to find senior ai software engineer visa sponsorship jobs in Florida?
Migrate Mate is built specifically for international job seekers looking for visa sponsorship roles in the U.S. You can filter directly for senior AI software engineer positions in Florida, seeing only employers with documented sponsorship activity. This saves significant time compared to manually reviewing H-1B disclosure data. Migrate Mate also surfaces roles from defense, fintech, and healthtech employers across Miami, Tampa, and Orlando that actively hire sponsored senior AI talent.
Are there state-specific considerations for senior AI software engineer sponsorship in Florida?
Florida has no state income tax, which affects prevailing wage comparisons since DOL wage determinations are based on the local metropolitan area, not state tax treatment. Senior AI engineer roles in defense and aerospace often require U.S. security clearances, which can limit sponsorship eligibility for certain visa holders depending on the role's clearance level. Florida's large university system, including UCF, University of Florida, and University of Miami, produces a consistent pipeline of AI talent that keeps employer sponsorship infrastructure active.
What is the prevailing wage for sponsored senior ai software 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.