TN Visa Full Stack Product Engineer Jobs
Full Stack Product Engineer roles qualify for TN visa sponsorship under the USMCA treaty, typically classified under the Computer Systems Analyst or Engineer category. Canadian citizens can secure TN status at the border or a U.S. port of entry, while Mexican nationals require consular processing. No lottery, no annual cap for Canadians.
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Get Access To All JobsWHAT MAKES US A GREAT PLACE TO WORK
We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.
Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
WHO YOU’LL WORK WITH
As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.
Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making.
The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.
WHERE YOU’LL FIT WITHIN THE TEAM
Senior Full-Stack AI Product Engineers design, build, and operate end-to-end AI product experiences across the PE due diligence platform. This role sits at the intersection of product engineering and applied AI, combining strong full-stack software engineering with deep practical knowledge of how LLMs and agentic systems behave in production.
You will own intelligent product features from backend orchestration through frontend experience, building the services, workflows, and user interfaces that make AI useful, reliable, and intuitive for end users. This includes designing agent workflows, retrieval pipelines, evaluation gates, human-in-the-loop review patterns, and the analyst-facing experiences that surface them. You are deeply technical about production-grade AI systems, but equally strong in translating non-deterministic model behavior into clear, trustworthy, and effective product experiences.
You will mentor other engineers, raise standards for AI product engineering, and help define how Bain builds safe, observable, and scalable AI-powered workflows.
Full-Stack AI Product Engineering (60%)
- Design and build end-to-end AI product features across backend services, orchestration layers, and frontend user experiences.
- Develop analyst-facing and internal AI interfaces for workflows such as deal screening, commercial due diligence research, document extraction, and portfolio monitoring.
- Build responsive, high-quality frontend experiences for streaming AI responses, structured outputs, source grounding, review and approval flows, and human-in-the-loop interactions.
- Implement full-stack application patterns for chat, copilot, workspace, and review-based AI experiences, including state management, real-time updates, and error handling.
- Partner closely with Product, Design, and domain stakeholders to translate ambiguous AI capabilities into intuitive, polished user experiences.
- Design and maintain stable contracts between frontend applications and AI/backend services, ensuring outputs are structured, testable, and resilient.
- Build contribution workflows and product surfaces for the Prompt Execution Sandbox and AI Artifact Studio, enabling safe and scalable use by non-engineers where required.
- Ensure AI product features are accessible, observable, and production-ready, with strong attention to usability, reliability, and edge-case handling.
AI Platform and Agent Workflow Engineering (40%)
- Design and build the Agent Gateway service, including inbound APIs, model routing, context management, response validation, and cost/audit logging.
- Build and maintain LangGraph agent workflows for PE use cases, including streaming, tool-calling, multi-step execution, and human-in-the-loop interrupt patterns.
- Integrate Temporal durable execution with LangGraph, including workflow and activity authoring, checkpointing strategies, retry and backoff policies, signal/query handling, and safe versioning for in-flight workflows.
- Build and maintain AI platform services such as Agent Session Manager, Memory Service, HITL Coordination Service, and Feedback/Correction Service with production SLO expectations.
- Design and implement RAG pipelines end-to-end, including chunking strategies, embedding model selection, vector store integration, re-ranking, and retrieval quality evaluation.
- Own evaluation and regression gates, including golden dataset management, metric definition, qualitative and quantitative evaluation, LangSmith/Braintrust integration, and CI enforcement on quality regressions.
- Implement context window management strategies such as token budgeting, truncation/compression, and tool-call state persistence to support reliability in longer-running workflows.
- Instrument AI services with structured logging, traces, and metrics, and define operational dashboards and alerts for latency, quality, cost, and failure signals.
- Deploy and operate AI workloads in Kubernetes, including containerization, Helm-based deployment patterns, and autoscaling considerations where appropriate.
Leadership, Collaboration, and Engineering Standards
- Mentor engineers and help set standards for production AI product engineering across testing, evaluation, documentation, safety constraints, and maintainability.
- Partner with Data Platform on feature store access patterns, inference integration, schemas, data contracts, and SLAs.
- Collaborate with Product Engineering and Design on AI feature surfacing, including streaming experiences, structured output rendering, citation and evidence UX, and HITL review interfaces.
- Use AI coding assistants to accelerate prototyping and development, while validating all production artifacts against testing and evaluation gates before promotion.
- Document agent behavior specifications, tool contracts, and product interaction patterns so behavior is explicit, reviewable, and maintainable.
ABOUT YOU
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
- 6+ years of experience building production software, including significant experience delivering full-stack applications and/or AI-enabled systems in production environments.
- Demonstrated experience shipping user-facing AI product features end-to-end, from backend orchestration and service design through frontend implementation and rollout.
- Experience delivering agentic systems into production, including tool calling, multi-step workflows, RAG, structured output handling, and measurable quality controls.
- Experience designing and operating evaluation frameworks, including golden datasets, regression gates, qualitative evaluation approaches, and CI controls that prevent quality regressions.
- Experience operating distributed services in containerized environments such as Docker and Kubernetes, with ownership of monitoring, reliability, and incident response.
- Demonstrated ability to mentor engineers and raise engineering standards through code review, design guidance, shared conventions, and strong technical judgement.
Full-Stack Product Engineering
- Strong experience building modern full-stack applications with robust frontend architecture and backend integration.
- Strong frontend engineering skills, including React and/or Next.js, TypeScript, component-based UI development, API integration, and application state management.
- Experience building polished product experiences for complex workflows, including tables, document-centric interfaces, review flows, workspace patterns, and real-time or streaming interactions.
- Ability to design UX for AI systems, including confidence indicators, citations/source grounding, fallback states, edit/retry patterns, and human review steps.
- Strong product sense and judgement in translating non-deterministic AI behavior into usable and trustworthy product experiences.
AI Platform Engineering
- Strong Python proficiency, including FastAPI, Pydantic v2, async patterns, pytest, Ruff, and mypy (strict).
- Deep hands-on experience with LangChain and/or LangGraph, including stateful graph construction, tool integration, checkpointing, HITL interrupts, and streaming patterns.
- Familiarity with Google ADK or equivalent agentic orchestration frameworks.
- Experience with Temporal, including workflow/activity authoring, signal/query handling, safe versioning strategies, and durable execution patterns.
- Strong prompt engineering skills, including structured output design, system prompt construction, instruction clarity, sampling configuration, and multi-turn context management.
- Experience designing and implementing RAG pipelines, including chunking, embedding selection, vector store integration, re-ranking, and retrieval quality evaluation.
- Experience designing LLM evaluation systems, including golden dataset design, metric definition, LangSmith/Braintrust integration, and regression gate implementation.
- Experience with context window management, including token budgeting, sliding-window truncation, semantic compression, and tool-call state persistence.
- Experience with vector databases such as pgvector and/or OpenSearch, including schema design, recall/precision tuning, and embedding lifecycle management.
- Experience containerizing and operating AI workloads using Docker and Kubernetes, including Helm charts and autoscaling concepts.
Generative AI and Agentic Systems
- Uses AI coding assistants such as Cursor and GitHub Copilot as a core part of the development workflow, while applying strong judgement about where generated code is reliable versus where it requires scrutiny.
- Designs multi-agent systems with clear orchestration logic, specialist sub-agent boundaries, tool interfaces, and failure-handling patterns.
- Builds and maintains evaluation pipelines that combine deterministic metrics with LLM-as-judge patterns for qualitative assessment.
- Capable of evaluating AI-generated infrastructure and workflow code, including Kubernetes manifests, Terraform, prompts, and agent graphs, for correctness, safety, and operability before production release.
General
- Treats non-determinism as a first-class engineering challenge and designs systems that degrade gracefully when model outputs are unexpected.
- Writes evaluation tests before shipping new AI capabilities, not after.
- Prototypes quickly using AI tooling, but validates every production artifact against defined quality gates before promotion.
- Documents behavior specifications, tool contracts, and user-facing interaction patterns rather than leaving critical behavior implicit in code.
- This role follows a hybrid model, requiring in-office presence at least 1 day per week.
U.S. COMPENSATION INFORMATION
Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).
Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:
In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $140,875 - $153,750
In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $147,625 - $161,250
In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $155,125 - $169,250
Placement within these ranges will vary based on factors such as experience, education, training, and skill level.
Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.
Annual discretionary performance bonus
This role may also be eligible for other elements of discretionary compensation
4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.
Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
Generous paid time off, including parental leave, sick leave and paid holidays
Fully vested 401(k) company contribution
Paid Life and Long-Term Disability insurance
Annual fitness reimbursements
WHAT MAKES US A GREAT PLACE TO WORK
We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.
Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
WHO YOU’LL WORK WITH
As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.
Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making.
The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.
WHERE YOU’LL FIT WITHIN THE TEAM
Senior Full-Stack AI Product Engineers design, build, and operate end-to-end AI product experiences across the PE due diligence platform. This role sits at the intersection of product engineering and applied AI, combining strong full-stack software engineering with deep practical knowledge of how LLMs and agentic systems behave in production.
You will own intelligent product features from backend orchestration through frontend experience, building the services, workflows, and user interfaces that make AI useful, reliable, and intuitive for end users. This includes designing agent workflows, retrieval pipelines, evaluation gates, human-in-the-loop review patterns, and the analyst-facing experiences that surface them. You are deeply technical about production-grade AI systems, but equally strong in translating non-deterministic model behavior into clear, trustworthy, and effective product experiences.
You will mentor other engineers, raise standards for AI product engineering, and help define how Bain builds safe, observable, and scalable AI-powered workflows.
Full-Stack AI Product Engineering (60%)
- Design and build end-to-end AI product features across backend services, orchestration layers, and frontend user experiences.
- Develop analyst-facing and internal AI interfaces for workflows such as deal screening, commercial due diligence research, document extraction, and portfolio monitoring.
- Build responsive, high-quality frontend experiences for streaming AI responses, structured outputs, source grounding, review and approval flows, and human-in-the-loop interactions.
- Implement full-stack application patterns for chat, copilot, workspace, and review-based AI experiences, including state management, real-time updates, and error handling.
- Partner closely with Product, Design, and domain stakeholders to translate ambiguous AI capabilities into intuitive, polished user experiences.
- Design and maintain stable contracts between frontend applications and AI/backend services, ensuring outputs are structured, testable, and resilient.
- Build contribution workflows and product surfaces for the Prompt Execution Sandbox and AI Artifact Studio, enabling safe and scalable use by non-engineers where required.
- Ensure AI product features are accessible, observable, and production-ready, with strong attention to usability, reliability, and edge-case handling.
AI Platform and Agent Workflow Engineering (40%)
- Design and build the Agent Gateway service, including inbound APIs, model routing, context management, response validation, and cost/audit logging.
- Build and maintain LangGraph agent workflows for PE use cases, including streaming, tool-calling, multi-step execution, and human-in-the-loop interrupt patterns.
- Integrate Temporal durable execution with LangGraph, including workflow and activity authoring, checkpointing strategies, retry and backoff policies, signal/query handling, and safe versioning for in-flight workflows.
- Build and maintain AI platform services such as Agent Session Manager, Memory Service, HITL Coordination Service, and Feedback/Correction Service with production SLO expectations.
- Design and implement RAG pipelines end-to-end, including chunking strategies, embedding model selection, vector store integration, re-ranking, and retrieval quality evaluation.
- Own evaluation and regression gates, including golden dataset management, metric definition, qualitative and quantitative evaluation, LangSmith/Braintrust integration, and CI enforcement on quality regressions.
- Implement context window management strategies such as token budgeting, truncation/compression, and tool-call state persistence to support reliability in longer-running workflows.
- Instrument AI services with structured logging, traces, and metrics, and define operational dashboards and alerts for latency, quality, cost, and failure signals.
- Deploy and operate AI workloads in Kubernetes, including containerization, Helm-based deployment patterns, and autoscaling considerations where appropriate.
Leadership, Collaboration, and Engineering Standards
- Mentor engineers and help set standards for production AI product engineering across testing, evaluation, documentation, safety constraints, and maintainability.
- Partner with Data Platform on feature store access patterns, inference integration, schemas, data contracts, and SLAs.
- Collaborate with Product Engineering and Design on AI feature surfacing, including streaming experiences, structured output rendering, citation and evidence UX, and HITL review interfaces.
- Use AI coding assistants to accelerate prototyping and development, while validating all production artifacts against testing and evaluation gates before promotion.
- Document agent behavior specifications, tool contracts, and product interaction patterns so behavior is explicit, reviewable, and maintainable.
ABOUT YOU
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
- 6+ years of experience building production software, including significant experience delivering full-stack applications and/or AI-enabled systems in production environments.
- Demonstrated experience shipping user-facing AI product features end-to-end, from backend orchestration and service design through frontend implementation and rollout.
- Experience delivering agentic systems into production, including tool calling, multi-step workflows, RAG, structured output handling, and measurable quality controls.
- Experience designing and operating evaluation frameworks, including golden datasets, regression gates, qualitative evaluation approaches, and CI controls that prevent quality regressions.
- Experience operating distributed services in containerized environments such as Docker and Kubernetes, with ownership of monitoring, reliability, and incident response.
- Demonstrated ability to mentor engineers and raise engineering standards through code review, design guidance, shared conventions, and strong technical judgement.
Full-Stack Product Engineering
- Strong experience building modern full-stack applications with robust frontend architecture and backend integration.
- Strong frontend engineering skills, including React and/or Next.js, TypeScript, component-based UI development, API integration, and application state management.
- Experience building polished product experiences for complex workflows, including tables, document-centric interfaces, review flows, workspace patterns, and real-time or streaming interactions.
- Ability to design UX for AI systems, including confidence indicators, citations/source grounding, fallback states, edit/retry patterns, and human review steps.
- Strong product sense and judgement in translating non-deterministic AI behavior into usable and trustworthy product experiences.
AI Platform Engineering
- Strong Python proficiency, including FastAPI, Pydantic v2, async patterns, pytest, Ruff, and mypy (strict).
- Deep hands-on experience with LangChain and/or LangGraph, including stateful graph construction, tool integration, checkpointing, HITL interrupts, and streaming patterns.
- Familiarity with Google ADK or equivalent agentic orchestration frameworks.
- Experience with Temporal, including workflow/activity authoring, signal/query handling, safe versioning strategies, and durable execution patterns.
- Strong prompt engineering skills, including structured output design, system prompt construction, instruction clarity, sampling configuration, and multi-turn context management.
- Experience designing and implementing RAG pipelines, including chunking, embedding selection, vector store integration, re-ranking, and retrieval quality evaluation.
- Experience designing LLM evaluation systems, including golden dataset design, metric definition, LangSmith/Braintrust integration, and regression gate implementation.
- Experience with context window management, including token budgeting, sliding-window truncation, semantic compression, and tool-call state persistence.
- Experience with vector databases such as pgvector and/or OpenSearch, including schema design, recall/precision tuning, and embedding lifecycle management.
- Experience containerizing and operating AI workloads using Docker and Kubernetes, including Helm charts and autoscaling concepts.
Generative AI and Agentic Systems
- Uses AI coding assistants such as Cursor and GitHub Copilot as a core part of the development workflow, while applying strong judgement about where generated code is reliable versus where it requires scrutiny.
- Designs multi-agent systems with clear orchestration logic, specialist sub-agent boundaries, tool interfaces, and failure-handling patterns.
- Builds and maintains evaluation pipelines that combine deterministic metrics with LLM-as-judge patterns for qualitative assessment.
- Capable of evaluating AI-generated infrastructure and workflow code, including Kubernetes manifests, Terraform, prompts, and agent graphs, for correctness, safety, and operability before production release.
General
- Treats non-determinism as a first-class engineering challenge and designs systems that degrade gracefully when model outputs are unexpected.
- Writes evaluation tests before shipping new AI capabilities, not after.
- Prototypes quickly using AI tooling, but validates every production artifact against defined quality gates before promotion.
- Documents behavior specifications, tool contracts, and user-facing interaction patterns rather than leaving critical behavior implicit in code.
- This role follows a hybrid model, requiring in-office presence at least 1 day per week.
U.S. COMPENSATION INFORMATION
Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).
Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:
In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $140,875 - $153,750
In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $147,625 - $161,250
In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $155,125 - $169,250
Placement within these ranges will vary based on factors such as experience, education, training, and skill level.
Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.
Annual discretionary performance bonus
This role may also be eligible for other elements of discretionary compensation
4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.
Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
Generous paid time off, including parental leave, sick leave and paid holidays
Fully vested 401(k) company contribution
Paid Life and Long-Term Disability insurance
Annual fitness reimbursements
See all 345+ Full Stack Product Engineer jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Full Stack Product Engineer roles.
Get Access To All JobsTips for Finding TN Visa Sponsorship as a Full Stack Product Engineer
Align your credentials to TN categories
TN classification for full stack roles flows through Engineer or Computer Systems Analyst categories. Your degree must match: a computer science, software engineering, or closely related field. A business or general IT degree without a technical specialization can trigger a denial.
Target employers with recent visa filing history
Companies that have recent visa filings for software engineering roles demonstrate operational readiness to sponsor work visas, including TN candidates. Focusing on employers experienced with visa sponsorship helps you identify companies prepared to support your TN application at the port of entry or consulate.
Search TN-specific roles through Migrate Mate
Migrate Mate filters full stack engineering jobs by employer experience with visa sponsorship, so you're not cold-applying to companies unfamiliar with work visa processes. Use it to identify roles where the hiring team already understands sponsorship requirements and can prepare the necessary support letter for your TN application.
Prepare a role-specific support letter before interviews
CBP officers adjudicate TN petitions at the port of entry using the employer's support letter. That letter must connect your full stack duties to the TN category definition, not just list tech stack. Generic offer letters are a leading cause of TN denials at the border.
Clarify Canadian versus Mexican processing with your employer
Canadian citizens can request TN status directly at a land border or pre-clearance airport with documents in hand. Mexican nationals must obtain a visa stamp at a U.S. consulate before entering. Employers often assume the same process applies to both, which delays timelines for Mexican applicants.
Request TN amendments when your role scope changes
If your employer reshuffles your product responsibilities after you start, a materially different role from what CBP approved can invalidate your TN status. File an amended petition through USCIS before the scope shift, not after your next international trip triggers a re-inspection.
Full Stack Product Engineer jobs are hiring across the US. Find yours.
Find Full Stack Product Engineer JobsFull Stack Product Engineer TN Visa: Frequently Asked Questions
Does a Full Stack Product Engineer role qualify for TN visa status?
Yes, provided the role maps to a qualifying TN category such as Computer Systems Analyst or Engineer. The job duties must require theoretical and practical application of highly specialized computer science knowledge. Roles that blend general product management with light coding often face scrutiny, so the job description needs to emphasize the engineering function, not the product strategy component.
How does the TN visa compare to H-1B for full stack engineering roles?
The TN visa has no annual lottery and no numerical cap for Canadian citizens, making it immediately accessible for qualified full stack engineers. H-1B requires entering a randomized lottery with roughly a 25 percent selection rate in recent years, adding a year or more of uncertainty. TN is employer-specific and role-specific, but the absence of lottery risk makes it a faster path for Canadians and Mexicans who qualify.
Can I switch full stack engineering jobs while on TN status?
You can change employers, but your TN status is tied to the original employer and role. You need a new TN approval before your first day with the new company. Canadian citizens can obtain a new TN at the border with updated employer documentation. Mexican nationals must return to a U.S. consulate for a new visa stamp before starting the new position.
Where can I find Full Stack Product Engineer jobs with TN visa sponsorship?
Migrate Mate is built specifically to surface engineering jobs where employers are already familiar with TN visa sponsorship under USMCA. Most general job boards don't filter by visa category, so you end up cold-applying to companies that have never sponsored a TN worker. Searching through a TN-specific platform saves weeks of back-and-forth with recruiters who don't know the process.
What documents do I need for a Full Stack Product Engineer TN application?
You need a detailed support letter from your employer that connects your full stack duties to the qualifying TN category, your university degree credential in a relevant field, and a valid passport. If your degree is not from a Canadian or U.S. institution, a credential evaluation from a recognized evaluation service strengthens your file. Canadian citizens present these at the port of entry; Mexican nationals submit them as part of the consular visa application.
See which Full Stack Product Engineer employers are hiring and sponsoring visas right now.
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