TN Visa AI Engineer Jobs
AI Engineer roles qualify for TN visa sponsorship under the USMCA's Computer Systems Analyst category, giving Canadian citizens a cap-free, lottery-free path to U.S. employment. Mexican citizens have a 5,500-per-year allocation. Employers file your TN documentation alongside a detailed job offer letter outlining your systems analysis duties.
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Get Access To All JobsAbout Luma Financial Technologies
Founded in 2018, Luma Financial Technologies (“Luma”) has pioneered a cutting-edge fintech software platform that has been adopted by broker/dealer firms, RIA offices, and private banks around the world. By using Luma, institutional and retail investors have a fully customizable, independent, buy-side technology platform that helps financial teams more efficiently learn about, research, purchase, and manage alternative investments as well as annuities. Luma gives these users the ability to oversee the full, end-to-end process lifecycle by offering a suite of solutions. These include education resources and training materials; creation and pricing of custom structured products; electronic order entry; and post-trade management. By prioritizing transparency and ease of use, Luma is a multi-issuer, multi-wholesaler, and multi-product option that advisors can utilize to best meet their clients’ specific portfolio needs. Headquartered in Cincinnati, OH, Luma also has offices in New York, NY, Miami, FL, Zurich, Switzerland and Lisbon, Portugal.
About The Role
Luma Fintech is building a best-in-class LLM-powered document parsing pipeline that extracts structured data from complex financial product term sheets. We are seeking an AI Quality Engineer to own the daily testing, analysis, and iterative improvement of our Claude API-based extraction system. This role sits at the intersection of financial data operations and applied AI, you will be the person who closes the loop between what the model outputs and what the schema demands.
- Run daily accuracy evaluations against a defined extraction schema, tracking field-level performance across structured product types (autocallables, CLNs, barrier notes, etc.)
- Design and maintain test cases, regression suites, and gold-standard document sets to benchmark extraction quality over time
- Diagnose extraction failures, distinguishing between prompt logic issues, schema ambiguity, model hallucinations, and edge-case document formats
- Iterate on prompt engineering, system instructions, and context design to improve field-level extraction accuracy
- Work alongside the AI Engineer lead to feed findings into validation logic and rules-based layers that sit on top of LLM output
- Document failure modes with reproducible examples and root-cause hypotheses
- Build and maintain evaluation metrics (precision, recall, field coverage, hallucination rate) and report on accuracy trends
- Flag schema gaps or ambiguities surfaced by real document variance and collaborate with data operations to refine field definitions
- Contribute to RAG improvements by identifying where retrieved context is insufficient or misleading
Qualifications
Required
- Hands-on experience working with LLM APIs (Anthropic, OpenAI, or similar) in a production or near-production context
- Strong prompt engineering skills, you understand how instruction design affects model behavior, not just output tone
- Analytical mindset with the ability to systematically isolate variables in model output quality
- Experience designing structured test cases or evaluation frameworks (QA background is a plus)
- Familiarity with JSON schema, structured data output, and data validation patterns
- Ability to read and interpret complex financial or legal documents (term sheets, prospectuses, offering documents), prior financial services exposure strongly preferred
- Strong written communication; you’ll be documenting findings for both technical and non-technical stakeholders
Preferred
- Experience with RAG pipelines and retrieval evaluation
- Python proficiency for scripting evaluation workflows or parsing outputs
- Background in structured financial products (autocallables, structured notes, credit-linked notes)
- Familiarity with evaluation frameworks or tools (e.g., LangSmith, RAGAS, custom evals)
What Success Looks Like
In 90 days, you have established a repeatable daily evaluation process, a documented baseline of field-level accuracy across product types, and have driven at least one measurable improvement in extraction quality through prompt iteration.
Why This Role
This is a high-ownership position on a strategic automation initiative with direct visibility to leadership. You won’t be maintaining someone else’s test suite, you’re building the quality layer of a system that processes real financial data at scale. The role will evolve as the system matures, with opportunity to expand into evaluation infrastructure and model improvement strategy.
The Pay Range For This Role Is
90,000 - 115,000 USD per year (Cincinnati)
About Luma Financial Technologies
Founded in 2018, Luma Financial Technologies (“Luma”) has pioneered a cutting-edge fintech software platform that has been adopted by broker/dealer firms, RIA offices, and private banks around the world. By using Luma, institutional and retail investors have a fully customizable, independent, buy-side technology platform that helps financial teams more efficiently learn about, research, purchase, and manage alternative investments as well as annuities. Luma gives these users the ability to oversee the full, end-to-end process lifecycle by offering a suite of solutions. These include education resources and training materials; creation and pricing of custom structured products; electronic order entry; and post-trade management. By prioritizing transparency and ease of use, Luma is a multi-issuer, multi-wholesaler, and multi-product option that advisors can utilize to best meet their clients’ specific portfolio needs. Headquartered in Cincinnati, OH, Luma also has offices in New York, NY, Miami, FL, Zurich, Switzerland and Lisbon, Portugal.
About The Role
Luma Fintech is building a best-in-class LLM-powered document parsing pipeline that extracts structured data from complex financial product term sheets. We are seeking an AI Quality Engineer to own the daily testing, analysis, and iterative improvement of our Claude API-based extraction system. This role sits at the intersection of financial data operations and applied AI, you will be the person who closes the loop between what the model outputs and what the schema demands.
- Run daily accuracy evaluations against a defined extraction schema, tracking field-level performance across structured product types (autocallables, CLNs, barrier notes, etc.)
- Design and maintain test cases, regression suites, and gold-standard document sets to benchmark extraction quality over time
- Diagnose extraction failures, distinguishing between prompt logic issues, schema ambiguity, model hallucinations, and edge-case document formats
- Iterate on prompt engineering, system instructions, and context design to improve field-level extraction accuracy
- Work alongside the AI Engineer lead to feed findings into validation logic and rules-based layers that sit on top of LLM output
- Document failure modes with reproducible examples and root-cause hypotheses
- Build and maintain evaluation metrics (precision, recall, field coverage, hallucination rate) and report on accuracy trends
- Flag schema gaps or ambiguities surfaced by real document variance and collaborate with data operations to refine field definitions
- Contribute to RAG improvements by identifying where retrieved context is insufficient or misleading
Qualifications
Required
- Hands-on experience working with LLM APIs (Anthropic, OpenAI, or similar) in a production or near-production context
- Strong prompt engineering skills, you understand how instruction design affects model behavior, not just output tone
- Analytical mindset with the ability to systematically isolate variables in model output quality
- Experience designing structured test cases or evaluation frameworks (QA background is a plus)
- Familiarity with JSON schema, structured data output, and data validation patterns
- Ability to read and interpret complex financial or legal documents (term sheets, prospectuses, offering documents), prior financial services exposure strongly preferred
- Strong written communication; you’ll be documenting findings for both technical and non-technical stakeholders
Preferred
- Experience with RAG pipelines and retrieval evaluation
- Python proficiency for scripting evaluation workflows or parsing outputs
- Background in structured financial products (autocallables, structured notes, credit-linked notes)
- Familiarity with evaluation frameworks or tools (e.g., LangSmith, RAGAS, custom evals)
What Success Looks Like
In 90 days, you have established a repeatable daily evaluation process, a documented baseline of field-level accuracy across product types, and have driven at least one measurable improvement in extraction quality through prompt iteration.
Why This Role
This is a high-ownership position on a strategic automation initiative with direct visibility to leadership. You won’t be maintaining someone else’s test suite, you’re building the quality layer of a system that processes real financial data at scale. The role will evolve as the system matures, with opportunity to expand into evaluation infrastructure and model improvement strategy.
The Pay Range For This Role Is
90,000 - 115,000 USD per year (Cincinnati)
See all 241+ TN Visa AI Engineer Jobs
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Get Access To All JobsTips for Finding TN Visa Sponsorship as an AI Engineer
Frame your role as systems analysis
TN visas don't have an AI Engineer category. Your job offer letter must describe your duties in terms of analyzing, designing, and implementing computer-based systems. Titles like 'ML Engineer' or 'AI Researcher' need careful framing to fit the Computer Systems Analyst classification.
Gather credential documentation before applying
CBP officers adjudicate TN applications on the spot at ports of entry for Canadians. Have your bachelor's degree transcript, diploma, and any credential evaluation ready before you start interviewing. Missing documents can result in same-day denial with no appeal window.
Target employers with recent visa sponsorship experience
Not every tech employer understands TN sponsorship. Prioritize companies with recent visa filings, since their HR and legal teams are already familiar with work visa processes and won't delay your offer over unfamiliar sponsorship requirements. Companies experienced with visa sponsorship are more likely to smoothly handle the TN support letter and border/consulate presentation process.
Use Migrate Mate to surface TN-ready employers
Identifying which employers actively sponsor TN visas for AI roles takes more than a keyword search. Migrate Mate filters job opportunities by TN sponsorship eligibility, connecting you directly with employers who have confirmed sponsorship pathways for Canadian and Mexican professionals.
Negotiate who handles the TN petition for Mexicans
Mexican nationals can't self-petition at the border like Canadians. Your employer must file a petition with USCIS before you can apply at a U.S. consulate. Confirm early in the offer process that your employer is prepared to file through USCIS rather than assuming border entry is an option.
Request a detailed duty-specific offer letter
Generic tech offer letters listing 'software development' responsibilities routinely trigger CBP scrutiny for AI Engineer TN applications. Ask your employer to itemize duties explicitly: data pipeline architecture, model deployment, and systems integration all map more cleanly to the Computer Systems Analyst category than vague engineering language.
AI Engineer TN Visa: Frequently Asked Questions
Does an AI Engineer role qualify for TN visa sponsorship?
Yes, but not under a dedicated AI Engineer category. TN visa eligibility for AI Engineers depends on demonstrating that the role fits the Computer Systems Analyst occupation listed in USMCA Appendix 2. Your employer's offer letter must describe duties involving the analysis, design, and implementation of computer-based systems. Titles alone don't determine eligibility; the actual job duties do.
How does TN visa sponsorship compare to H-1B for AI Engineers?
TN visa sponsorship has no annual lottery, no cap for Canadians, and no multi-month waiting period. H-1B visa selection is randomized and capped at 85,000 per fiscal year. Canadian AI Engineers using TN can often start within days of a border appointment approval. Mexican nationals still go through USCIS petition processing for TN, but avoid the lottery entirely.
Where can Canadian and Mexican AI Engineers find TN-sponsored jobs?
Migrate Mate is built specifically for Canadian and Mexican professionals seeking TN visa sponsorship. It surfaces employers with active TN sponsorship pathways for technology and engineering roles, including AI Engineer positions. Standard job boards don't filter by visa sponsorship type, making it difficult to identify which employers will actually support a TN application versus simply being open to 'work authorization.'
Can a Canadian AI Engineer apply for TN status at the border without a lawyer?
Yes. Canadian citizens apply at a U.S. port of entry or pre-clearance location and receive a same-day decision from CBP. You bring your job offer letter, educational credentials, and passport. There's no USCIS petition filing required for Canadians. That said, the offer letter must be precise about your systems analysis duties, and a poorly worded letter is the most common reason for denial.
What happens if my AI Engineer TN visa application is denied at the border?
CBP denial at the port of entry means you return to Canada the same day. There's no formal appeal process for TN border adjudications. You can reapply immediately at the same or a different port of entry with corrected documentation, typically an updated offer letter that more clearly ties your duties to the Computer Systems Analyst category. Denial doesn't carry a waiting period or ban.