AI Data Engineer Visa Sponsorship Jobs in Florida
Florida's growing tech sector, anchored by Miami's emerging AI hub, Tampa's financial technology firms, and Orlando's simulation and defense industry, is generating steady demand for AI data engineers. Major employers including Chewy, Carnival Corporation, and University of Florida Health sponsor H-1B and other work visas for qualified candidates in this role.
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Get Access To All JobsClinical AI Data Engineer
Location: Remote work - Tampa, FL Preferred
Type: Full-time
Salary Range: $130 - $170k plus bonus (experience dependent)
About us
Ascend Technologies Group (ATG) is a U.S.-based provider of managed IT and cloud services, specializing in telecom and data solutions. We serve clients across the U.S. with tailored strategies for enterprise accounts, focusing on cost optimization, proactive monitoring, and efficient resource use. We emphasize precise, measurable outcomes delivered on time and within budget, supported by 24/7 technical assistance, network operations, and advanced data management. Our mission is to empower businesses through innovative technology, prioritizing security and efficiency to ensure data safety and accessibility. Our core values include excellence in solutions, innovation to optimize ecosystems, professionalism through respect and integrity, intentional action with clarity, impactful growth and accountability, and strategic expertise for precise results.
About the Role
We're seeking a Clinical AI Data Engineer to build production-grade LLM systems that extract, structure, and validate cancer-related data from electronic health records. You'll work at the intersection of oncology, applied AI, and clinical informatics—developing intelligent agents that transform unstructured clinical documentation into research-ready datasets. This role emphasizes deep understanding of oncology data structures and advanced LLM techniques over traditional data engineering.
Key Responsibilities
Build Production LLM Systems for Oncology Data Extraction
Design and deploy AI systems that reliably extract oncology-specific information from clinical notes and reports, including staging classifications, biomarker results, treatment regimens, and patient and outcomes.
Develop Robust AI Agents for Medical Reasoning
Create AI agents that handle complex clinical tasks: multi-document synthesis across patient charts, precise entity and relationship extraction for cancer phenotypes, and long-context understanding of treatment histories spanning years. Navigate the nuances of oncology terminology and clinical reasoning patterns.
Ensure Clinical Accuracy and Reliability
Develop strategies to minimize hallucinations, improve factual consistency, and gracefully handle ambiguous or incomplete clinical documentation. Build evaluation frameworks that measure precision, recall, and clinical validity against gold-standard oncology annotations.
Master Oncology EHR Data Structures
Work deeply with cancer-specific EHR data including pathology reports, radiology imaging summaries, genomic test results, and treatment documentation. Understand relationships between diagnosis codes, medication orders, lab values, and clinical narratives.
Drive Technical Excellence Through LLM Experimentation
Establish benchmarking standards and evaluation metrics for clinical NLP models. Experiment with advanced prompting techniques, retrieval-augmented generation, fine-tuning approaches, and multi-agent architectures. Conduct hands-on analysis to identify edge cases, model drift, and opportunities for improvement.
Bridge Clinical and Technical Domains
Collaborate with oncologists, clinical data abstraction leads, and product managers to translate complex clinical requirements into technical solutions, iterating based on real-world feedback.
Required Qualifications
- 5+ years writing production Python code with proven experience shipping AI/ML systems to production
- 3+ years hands-on experience with LLMs —including advanced prompt engineering, function calling, agent frameworks, retrieval strategies, fine-tuning, systematic failure mode analysis, and developing intuition for achieving reliable results in production environments
- Deep oncology EHR expertise: Strong understanding of cancer EHR data structures, oncology terminologies (ICD-O, SNOMED, AJCC staging, RECIST criteria), clinical documentation workflows, and how oncology data flows through health systems
- Advanced NLP and semantic understanding: Deep expertise in information extraction, entity recognition, relationship mapping, and clinical NLP challenges specific to cancer care documentation
- Production AI agent experience: You've built agents that work reliably in real-world clinical environments, not just demos—with practical experience handling multi-step reasoning, tool use, and error recovery
- Clinical reasoning skills: Ability to understand oncology treatment pathways, interpret clinical notes, and recognize clinically meaningful patterns in unstructured documentation
- Scientific approach: You formulate hypotheses, design rigorous experiments, and iterate based on empirical evidence
- Clear communicator: Can explain complex technical tradeoffs to both clinical stakeholders and non-technical audiences
- Ability to thrive in a fast-paced, collaborative, and remote-first environment
Preferred Qualifications
- Experience with Snowflake or similar data warehouse platforms
- Direct experience working with oncology EHR data
- Familiarity with NGS data interpretation, and precision oncology concepts
- Experience with knowledge graphs and relationship mapping in medical contexts
- Familiarity with SOC-2, HIPAA, or sensitive healthcare data handling requirements
- Master's or PhD in computational biology, bioinformatics, or related quantitative field
- Knowledge of cancer treatment guidelines (NCCN, ASCO) and oncology clinical workflows
What We Value
Quality Obsessive — You ship clean, maintainable code and refuse to compromise on quality, knowing that clinical applications demand the highest standards.
Fast Executor — You break down complex problems, ship early iterations, and move quickly without over-engineering solutions.
Ruthless Prioritizer — You focus relentlessly on user and business value, staying aligned with clinical outcomes rather than getting distracted by interesting technical tangents.
Self-Directed — You thrive in ambiguity, define your own path forward, and drive projects to completion without requiring detailed specifications.
Strong Collaborator — You actively seek diverse perspectives, hold your opinions loosely, and optimize for team outcomes over individual ego.
Continuous Learner — You stay at the cutting edge of AI advances and clinical informatics, effectively translating new techniques and insights across the organization.
Clinical AI Data Engineer
Location: Remote work - Tampa, FL Preferred
Type: Full-time
Salary Range: $130 - $170k plus bonus (experience dependent)
About us
Ascend Technologies Group (ATG) is a U.S.-based provider of managed IT and cloud services, specializing in telecom and data solutions. We serve clients across the U.S. with tailored strategies for enterprise accounts, focusing on cost optimization, proactive monitoring, and efficient resource use. We emphasize precise, measurable outcomes delivered on time and within budget, supported by 24/7 technical assistance, network operations, and advanced data management. Our mission is to empower businesses through innovative technology, prioritizing security and efficiency to ensure data safety and accessibility. Our core values include excellence in solutions, innovation to optimize ecosystems, professionalism through respect and integrity, intentional action with clarity, impactful growth and accountability, and strategic expertise for precise results.
About the Role
We're seeking a Clinical AI Data Engineer to build production-grade LLM systems that extract, structure, and validate cancer-related data from electronic health records. You'll work at the intersection of oncology, applied AI, and clinical informatics—developing intelligent agents that transform unstructured clinical documentation into research-ready datasets. This role emphasizes deep understanding of oncology data structures and advanced LLM techniques over traditional data engineering.
Key Responsibilities
Build Production LLM Systems for Oncology Data Extraction
Design and deploy AI systems that reliably extract oncology-specific information from clinical notes and reports, including staging classifications, biomarker results, treatment regimens, and patient and outcomes.
Develop Robust AI Agents for Medical Reasoning
Create AI agents that handle complex clinical tasks: multi-document synthesis across patient charts, precise entity and relationship extraction for cancer phenotypes, and long-context understanding of treatment histories spanning years. Navigate the nuances of oncology terminology and clinical reasoning patterns.
Ensure Clinical Accuracy and Reliability
Develop strategies to minimize hallucinations, improve factual consistency, and gracefully handle ambiguous or incomplete clinical documentation. Build evaluation frameworks that measure precision, recall, and clinical validity against gold-standard oncology annotations.
Master Oncology EHR Data Structures
Work deeply with cancer-specific EHR data including pathology reports, radiology imaging summaries, genomic test results, and treatment documentation. Understand relationships between diagnosis codes, medication orders, lab values, and clinical narratives.
Drive Technical Excellence Through LLM Experimentation
Establish benchmarking standards and evaluation metrics for clinical NLP models. Experiment with advanced prompting techniques, retrieval-augmented generation, fine-tuning approaches, and multi-agent architectures. Conduct hands-on analysis to identify edge cases, model drift, and opportunities for improvement.
Bridge Clinical and Technical Domains
Collaborate with oncologists, clinical data abstraction leads, and product managers to translate complex clinical requirements into technical solutions, iterating based on real-world feedback.
Required Qualifications
- 5+ years writing production Python code with proven experience shipping AI/ML systems to production
- 3+ years hands-on experience with LLMs —including advanced prompt engineering, function calling, agent frameworks, retrieval strategies, fine-tuning, systematic failure mode analysis, and developing intuition for achieving reliable results in production environments
- Deep oncology EHR expertise: Strong understanding of cancer EHR data structures, oncology terminologies (ICD-O, SNOMED, AJCC staging, RECIST criteria), clinical documentation workflows, and how oncology data flows through health systems
- Advanced NLP and semantic understanding: Deep expertise in information extraction, entity recognition, relationship mapping, and clinical NLP challenges specific to cancer care documentation
- Production AI agent experience: You've built agents that work reliably in real-world clinical environments, not just demos—with practical experience handling multi-step reasoning, tool use, and error recovery
- Clinical reasoning skills: Ability to understand oncology treatment pathways, interpret clinical notes, and recognize clinically meaningful patterns in unstructured documentation
- Scientific approach: You formulate hypotheses, design rigorous experiments, and iterate based on empirical evidence
- Clear communicator: Can explain complex technical tradeoffs to both clinical stakeholders and non-technical audiences
- Ability to thrive in a fast-paced, collaborative, and remote-first environment
Preferred Qualifications
- Experience with Snowflake or similar data warehouse platforms
- Direct experience working with oncology EHR data
- Familiarity with NGS data interpretation, and precision oncology concepts
- Experience with knowledge graphs and relationship mapping in medical contexts
- Familiarity with SOC-2, HIPAA, or sensitive healthcare data handling requirements
- Master's or PhD in computational biology, bioinformatics, or related quantitative field
- Knowledge of cancer treatment guidelines (NCCN, ASCO) and oncology clinical workflows
What We Value
Quality Obsessive — You ship clean, maintainable code and refuse to compromise on quality, knowing that clinical applications demand the highest standards.
Fast Executor — You break down complex problems, ship early iterations, and move quickly without over-engineering solutions.
Ruthless Prioritizer — You focus relentlessly on user and business value, staying aligned with clinical outcomes rather than getting distracted by interesting technical tangents.
Self-Directed — You thrive in ambiguity, define your own path forward, and drive projects to completion without requiring detailed specifications.
Strong Collaborator — You actively seek diverse perspectives, hold your opinions loosely, and optimize for team outcomes over individual ego.
Continuous Learner — You stay at the cutting edge of AI advances and clinical informatics, effectively translating new techniques and insights across the organization.
AI Data Engineer Job Roles in Florida
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Search AI Data Engineer Jobs in FloridaAI Data Engineer Jobs in Florida: Frequently Asked Questions
Which companies in Florida sponsor visas for AI data engineers?
Florida-based employers with documented H-1B sponsorship history in data and AI roles include Chewy, Carnival Corporation, NextEra Energy, AdventHealth, and Citrix. Miami-headquartered tech firms and defense contractors in the Orlando corridor such as Lockheed Martin and L3Harris also regularly sponsor foreign nationals for AI and data engineering positions. University systems including the University of Florida and University of Miami sponsor roles through academic and research appointments as well.
Which visa types are most common for AI data engineer roles in Florida?
The H-1B is the most common visa for AI data 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 with advanced degrees may also be sponsored for O-1A visas if they have demonstrated exceptional ability. TN visas are available to Canadian and Mexican nationals working in qualifying engineering or computer-related occupations.
Which cities in Florida have the most AI data engineer sponsorship jobs?
Miami leads Florida for AI data engineer sponsorship activity, driven by its expanding tech startup ecosystem and the presence of regional headquarters for financial services and logistics firms. Tampa and St. Petersburg follow closely, with strong demand from fintech companies and healthcare networks. Orlando contributes sponsorship opportunities through its defense, simulation, and hospitality technology sectors. Jacksonville has a smaller but growing presence through banking and insurance employers.
How to find ai data engineer visa sponsorship jobs in Florida?
Migrate Mate is built specifically for international candidates seeking visa sponsorship and lets you filter AI data engineer roles by state, so you can view Florida-specific openings from employers that have a history of sponsoring work visas. Rather than sorting through general job listings with no indication of sponsorship intent, Migrate Mate surfaces roles where sponsorship is confirmed or highly likely, which is particularly useful in a competitive market like South Florida or Tampa Bay.
Are there any Florida-specific factors AI data engineers should know about when seeking sponsorship?
Florida has no state income tax, which affects prevailing wage calculations and how employers structure compensation packages relative to Department of Labor wage levels. The state's concentration of healthcare, hospitality, defense, and financial services industries means AI data engineer roles often require domain-specific experience in those sectors. Florida also has a strong international student pipeline through the University of Florida, Florida International University, and the University of Central Florida, creating active OPT-to-H-1B sponsorship pathways at many in-state employers.
What is the prevailing wage for sponsored ai data 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.
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