Solutions Architect Visa Sponsorship Jobs in Pennsylvania
Pennsylvania's solutions architect market centers on Philadelphia's financial and healthcare technology sectors and Pittsburgh's growing tech scene, with major employers like Comcast, Vanguard, Carnegie Mellon University's affiliated firms, and PNC Financial regularly hiring for these roles. The state's concentration of enterprise IT users across healthcare, finance, and higher education creates steady demand for solutions architects who need visa sponsorship.
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See all 99+ Solutions Architect Jobs in Pennsylvania with Visa Sponsorship
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Get Access To All JobsAt Five Below our growth is a result of the people who embrace our purpose: We know life is way better when you are free to Let Go & Have Fun in an amazing experience, filled with unlimited possibilities, priced so low, you can always say yes to the newest, coolest stuff! Just ask any of our over 27,000 associates who work at Five Below and they’ll tell you there’s no other place like it. It all starts with our purpose and then, The Five Below Way, which is our values and behaviors that each and every associate believes in.
It’s all about culture at Five Below, making this a place that can inspire you as much as you inspire us with big ideas, super energy, passion, and the ability to make the workplace a WOWplace!
Key Responsibilities
1. AI Architecture & Strategy
- Define and own the enterprise AI architecture for retail use cases, aligning with business priorities and technology strategy.
- Develop reference architectures, patterns, and standards for AI/ML and Generative AI solutions, with an emphasis on open-source-first design principles.
- Translate retail business problems — across merchandising, supply chain, stores, marketing, and e-commerce — into scalable AI solution blueprints.
- Partner with business and product leaders to identify and prioritize high-impact AI opportunities.
- Champion open-source AI frameworks and tooling (e.g., Hugging Face, LangChain, LlamaIndex, Ray, MLflow, Feast) as the default approach before evaluating commercial alternatives.
2. Open-Source AI Architecture
- Lead the selection, evaluation, and integration of open-source AI and ML frameworks into Company’s enterprise architecture.
- Design reusable patterns for open-source LLM deployment, fine-tuning, and serving (e.g., vLLM, Ollama, llama.cpp, OpenLLM).
- Establish governance standards for open-source model usage, including licensing review, security scanning, and model provenance tracking.
- Build internal capability around open-source foundations to reduce vendor lock-in and accelerate experimentation velocity.
- Evaluate and adopt emerging open-source agentic frameworks (e.g., AutoGen, CrewAI, LangGraph) for retail automation use cases.
3. AI Solutioning & Design
- Architect end-to-end AI solutions, including data ingestion, feature engineering, model training, inference, and system integration.
- Design AI systems for core retail domains such as:
- Search, recommendations, and personalization
- Demand forecasting, inventory optimization, replenishment, and allocation
- Pricing and markdown optimization
- AI assistants and copilots for store, merchandising, and supply-chain teams
- Define integration patterns between AI services and retail platforms (POS, OMS, WMS, CRM, e-commerce).
- Lead architectural reviews, ensuring solutions meet performance, scalability, security, cost, and reliability requirements.
4. AI Observability
- Define and implement an AI observability framework covering model performance monitoring, data drift detection, prediction quality tracking, and system health across all production AI systems.
- Establish real-time and batch monitoring pipelines for model inference using tools such as Evidently AI, Arize, WhyLogs, Fiddler, or equivalent open-source platforms.
- Design standardized dashboards and alerting for model degradation, data skew, latency SLO breaches, and feature store anomalies.
- Build feedback loop infrastructure to capture ground-truth labels and enable continuous model evaluation in production.
- Define observability standards for GenAI and LLM systems, including hallucination rate tracking, prompt/response logging, latency percentiles, and cost-per-query attribution.
- Partner with MLOps and Platform Engineering to embed observability as a first-class requirement in every AI system from Day 1.
5. AI Security
- Serve as the AI security authority for Company, owning the threat model for all AI and ML systems in production.
- Define and enforce secure-by-design standards for model development, training data handling, inference APIs, and GenAI integrations.
- Architect defenses against AI-specific attack vectors, including prompt injection, model inversion, adversarial inputs, data poisoning, and supply chain risks in open-source model adoption.
- Establish data privacy controls for AI pipelines, ensuring compliance with applicable regulations (e.g., CCPA) and internal data governance policies.
- Lead AI red-teaming and adversarial testing exercises to proactively identify and remediate security gaps before production deployment.
- Partner with Information Security, Legal, and Enterprise Risk to maintain an AI risk register and align AI security posture with the organization’s broader cybersecurity framework.
- Define guardrails, content filtering, and human-in-the-loop safeguards for all customer-facing and associate-facing GenAI applications.
6. MLOps, GenAI & Governance
- Establish MLOps and AIOps practices, including CI/CD for models, automated retraining, monitoring, drift detection, and cost controls.
- Define standards for Generative AI and LLM usage, including multi-RAG architectures, MCP, and vector search.
- Define prompt orchestration, tool-calling, and agentic workflow patterns.
- Ensure AI solutions comply with data privacy, security, and responsible AI principles.
- Partner with Security, Legal, and Enterprise Architecture to align AI solutions with governance and risk standards.
7. AI Productivity Tooling Mandate
- Personally mandate and model the daily use of AI-native productivity tools across all architecture and delivery work.
- Evaluate, recommend, and govern the enterprise use of tools including:
- Microsoft Copilot – for productivity, code assistance, and enterprise knowledge retrieval
- Cursor – for AI-assisted development and code generation within engineering workflows
- Glean – for enterprise search, institutional knowledge management, and AI-powered information retrieval
- Claude (Anthropic) – for complex reasoning, document synthesis, and agentic task automation
- Equivalent or emerging AI productivity platforms as the market evolves
- Define standards and guardrails for enterprise AI tool adoption, including data classification policies governing what information may be shared with each platform.
- Train and upskill engineering and cross-functional teams on effective use of AI productivity tooling to multiply output and reduce time-to-delivery.
8. Technology Evaluation, Implementation & Delivery
- Work closely with AI Engineers, ML Engineers, Data Engineers, and platform teams to ensure architectures are production-ready and executable.
- Provide hands-on guidance during implementation, including reference code, pipelines, schemas, and infrastructure patterns.
- Evaluate and recommend AI SaaS solutions, cloud services, and frameworks (AWS, Azure, GCP, Databricks, Snowflake, etc.).
- Lead build vs. buy vs. open-source decisions and support vendor selection for AI capabilities.
Required Qualifications
- 9+ years of experience in software, data, or AI engineering, with 5+ years in AI/ML architecture roles.
- Proven experience designing and delivering production AI solutions specifically in retail, e-commerce, supply chain, or consumer-facing industries — this is a non-negotiable requirement.
- Deep hands-on expertise with open-source AI/ML ecosystem: Hugging Face Transformers, LangChain, LlamaIndex, MLflow, Ray, Feast, Evidently, or equivalent.
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
- Experience with modern data architectures: lakehouse, streaming, batch pipelines; platforms such as Databricks and Snowflake.
- Demonstrated experience designing AI observability systems — including model monitoring, drift detection, and production feedback loops.
- Working knowledge of AI security threat models, including prompt injection, adversarial attacks, and secure LLM deployment practices.
- Hands-on experience with cloud platforms and managed AI/ML services (AWS SageMaker, Azure ML, Vertex AI, or equivalent).
- Established practice of using AI productivity tools (e.g., Copilot, Cursor, Claude, Glean, or similar) in daily engineering and architecture work.
- Excellent communication skills with the ability to explain complex architectures to both technical and business stakeholders.
Preferred Qualifications
- Experience building or scaling enterprise AI platforms or AI Centers of Excellence.
- Contributions to open-source AI projects or published architecture patterns.
- Experience with AI red-teaming, adversarial testing, or formal AI risk assessment frameworks.
- Familiarity with retail-specific platforms: Manhattan WMS, Blue Yonder, Aptos POS, Salesforce Commerce Cloud, or equivalent.
- Cloud or AI certifications (AWS ML Specialty, Azure AI Engineer, GCP Professional ML Engineer).
Explore our benefits site to discover all the perks and support we offer! From health coverage to financial and personal wellness, we've got you covered—check it out today! benefits.fivebelow.com/public/welcome
Five Below is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.
Five Below is committed to working with and providing reasonable accommodations for individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please submit a request and let us know the nature of your request and your contact information. crewservices.zendesk.com/hc/en-us/requests/new
BE AWARE OF FRAUD! Please be aware of potentially fraudulent job postings or suspicious recruiter activity by persons that are posing as a Five Below recruiters. Please confirm that the person you are working with has an @fivebelow.com email address. Additionally, Five Below does NOT request financial information or payments from candidates at any point during the hiring process. If you suspect fraudulent activity, please visit Five Below's Career Site to verify the posting. fivebelow.com/info/careers
At Five Below our growth is a result of the people who embrace our purpose: We know life is way better when you are free to Let Go & Have Fun in an amazing experience, filled with unlimited possibilities, priced so low, you can always say yes to the newest, coolest stuff! Just ask any of our over 27,000 associates who work at Five Below and they’ll tell you there’s no other place like it. It all starts with our purpose and then, The Five Below Way, which is our values and behaviors that each and every associate believes in.
It’s all about culture at Five Below, making this a place that can inspire you as much as you inspire us with big ideas, super energy, passion, and the ability to make the workplace a WOWplace!
Key Responsibilities
1. AI Architecture & Strategy
- Define and own the enterprise AI architecture for retail use cases, aligning with business priorities and technology strategy.
- Develop reference architectures, patterns, and standards for AI/ML and Generative AI solutions, with an emphasis on open-source-first design principles.
- Translate retail business problems — across merchandising, supply chain, stores, marketing, and e-commerce — into scalable AI solution blueprints.
- Partner with business and product leaders to identify and prioritize high-impact AI opportunities.
- Champion open-source AI frameworks and tooling (e.g., Hugging Face, LangChain, LlamaIndex, Ray, MLflow, Feast) as the default approach before evaluating commercial alternatives.
2. Open-Source AI Architecture
- Lead the selection, evaluation, and integration of open-source AI and ML frameworks into Company’s enterprise architecture.
- Design reusable patterns for open-source LLM deployment, fine-tuning, and serving (e.g., vLLM, Ollama, llama.cpp, OpenLLM).
- Establish governance standards for open-source model usage, including licensing review, security scanning, and model provenance tracking.
- Build internal capability around open-source foundations to reduce vendor lock-in and accelerate experimentation velocity.
- Evaluate and adopt emerging open-source agentic frameworks (e.g., AutoGen, CrewAI, LangGraph) for retail automation use cases.
3. AI Solutioning & Design
- Architect end-to-end AI solutions, including data ingestion, feature engineering, model training, inference, and system integration.
- Design AI systems for core retail domains such as:
- Search, recommendations, and personalization
- Demand forecasting, inventory optimization, replenishment, and allocation
- Pricing and markdown optimization
- AI assistants and copilots for store, merchandising, and supply-chain teams
- Define integration patterns between AI services and retail platforms (POS, OMS, WMS, CRM, e-commerce).
- Lead architectural reviews, ensuring solutions meet performance, scalability, security, cost, and reliability requirements.
4. AI Observability
- Define and implement an AI observability framework covering model performance monitoring, data drift detection, prediction quality tracking, and system health across all production AI systems.
- Establish real-time and batch monitoring pipelines for model inference using tools such as Evidently AI, Arize, WhyLogs, Fiddler, or equivalent open-source platforms.
- Design standardized dashboards and alerting for model degradation, data skew, latency SLO breaches, and feature store anomalies.
- Build feedback loop infrastructure to capture ground-truth labels and enable continuous model evaluation in production.
- Define observability standards for GenAI and LLM systems, including hallucination rate tracking, prompt/response logging, latency percentiles, and cost-per-query attribution.
- Partner with MLOps and Platform Engineering to embed observability as a first-class requirement in every AI system from Day 1.
5. AI Security
- Serve as the AI security authority for Company, owning the threat model for all AI and ML systems in production.
- Define and enforce secure-by-design standards for model development, training data handling, inference APIs, and GenAI integrations.
- Architect defenses against AI-specific attack vectors, including prompt injection, model inversion, adversarial inputs, data poisoning, and supply chain risks in open-source model adoption.
- Establish data privacy controls for AI pipelines, ensuring compliance with applicable regulations (e.g., CCPA) and internal data governance policies.
- Lead AI red-teaming and adversarial testing exercises to proactively identify and remediate security gaps before production deployment.
- Partner with Information Security, Legal, and Enterprise Risk to maintain an AI risk register and align AI security posture with the organization’s broader cybersecurity framework.
- Define guardrails, content filtering, and human-in-the-loop safeguards for all customer-facing and associate-facing GenAI applications.
6. MLOps, GenAI & Governance
- Establish MLOps and AIOps practices, including CI/CD for models, automated retraining, monitoring, drift detection, and cost controls.
- Define standards for Generative AI and LLM usage, including multi-RAG architectures, MCP, and vector search.
- Define prompt orchestration, tool-calling, and agentic workflow patterns.
- Ensure AI solutions comply with data privacy, security, and responsible AI principles.
- Partner with Security, Legal, and Enterprise Architecture to align AI solutions with governance and risk standards.
7. AI Productivity Tooling Mandate
- Personally mandate and model the daily use of AI-native productivity tools across all architecture and delivery work.
- Evaluate, recommend, and govern the enterprise use of tools including:
- Microsoft Copilot – for productivity, code assistance, and enterprise knowledge retrieval
- Cursor – for AI-assisted development and code generation within engineering workflows
- Glean – for enterprise search, institutional knowledge management, and AI-powered information retrieval
- Claude (Anthropic) – for complex reasoning, document synthesis, and agentic task automation
- Equivalent or emerging AI productivity platforms as the market evolves
- Define standards and guardrails for enterprise AI tool adoption, including data classification policies governing what information may be shared with each platform.
- Train and upskill engineering and cross-functional teams on effective use of AI productivity tooling to multiply output and reduce time-to-delivery.
8. Technology Evaluation, Implementation & Delivery
- Work closely with AI Engineers, ML Engineers, Data Engineers, and platform teams to ensure architectures are production-ready and executable.
- Provide hands-on guidance during implementation, including reference code, pipelines, schemas, and infrastructure patterns.
- Evaluate and recommend AI SaaS solutions, cloud services, and frameworks (AWS, Azure, GCP, Databricks, Snowflake, etc.).
- Lead build vs. buy vs. open-source decisions and support vendor selection for AI capabilities.
Required Qualifications
- 9+ years of experience in software, data, or AI engineering, with 5+ years in AI/ML architecture roles.
- Proven experience designing and delivering production AI solutions specifically in retail, e-commerce, supply chain, or consumer-facing industries — this is a non-negotiable requirement.
- Deep hands-on expertise with open-source AI/ML ecosystem: Hugging Face Transformers, LangChain, LlamaIndex, MLflow, Ray, Feast, Evidently, or equivalent.
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
- Experience with modern data architectures: lakehouse, streaming, batch pipelines; platforms such as Databricks and Snowflake.
- Demonstrated experience designing AI observability systems — including model monitoring, drift detection, and production feedback loops.
- Working knowledge of AI security threat models, including prompt injection, adversarial attacks, and secure LLM deployment practices.
- Hands-on experience with cloud platforms and managed AI/ML services (AWS SageMaker, Azure ML, Vertex AI, or equivalent).
- Established practice of using AI productivity tools (e.g., Copilot, Cursor, Claude, Glean, or similar) in daily engineering and architecture work.
- Excellent communication skills with the ability to explain complex architectures to both technical and business stakeholders.
Preferred Qualifications
- Experience building or scaling enterprise AI platforms or AI Centers of Excellence.
- Contributions to open-source AI projects or published architecture patterns.
- Experience with AI red-teaming, adversarial testing, or formal AI risk assessment frameworks.
- Familiarity with retail-specific platforms: Manhattan WMS, Blue Yonder, Aptos POS, Salesforce Commerce Cloud, or equivalent.
- Cloud or AI certifications (AWS ML Specialty, Azure AI Engineer, GCP Professional ML Engineer).
Explore our benefits site to discover all the perks and support we offer! From health coverage to financial and personal wellness, we've got you covered—check it out today! benefits.fivebelow.com/public/welcome
Five Below is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.
Five Below is committed to working with and providing reasonable accommodations for individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please submit a request and let us know the nature of your request and your contact information. crewservices.zendesk.com/hc/en-us/requests/new
BE AWARE OF FRAUD! Please be aware of potentially fraudulent job postings or suspicious recruiter activity by persons that are posing as a Five Below recruiters. Please confirm that the person you are working with has an @fivebelow.com email address. Additionally, Five Below does NOT request financial information or payments from candidates at any point during the hiring process. If you suspect fraudulent activity, please visit Five Below's Career Site to verify the posting. fivebelow.com/info/careers
Solutions Architect Job Roles in Pennsylvania
See all 99+ Solutions Architect Jobs in Pennsylvania
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Search Solutions Architect Jobs in PennsylvaniaSolutions Architect Jobs in Pennsylvania: Frequently Asked Questions
Which companies in Pennsylvania sponsor visas for solutions architects?
Large enterprises with established immigration programs are the most consistent sponsors in Pennsylvania. Comcast, Vanguard, PNC Financial, Independence Blue Cross, and Unisys have all filed H-1B visa petitions for solutions architect and related technical roles. Consulting firms with Pennsylvania offices, including Accenture, Cognizant, and Infosys, also sponsor regularly, as their delivery model depends on placing specialized technical talent with clients across the state.
Which visa types are most common for solutions architect roles in Pennsylvania?
The H-1B is the primary visa category for solutions architects in Pennsylvania, as the role consistently qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, engineering, or a related field. L-1B visas are also used when a candidate transfers from a multinational employer's foreign office. Candidates with Canadian or Mexican citizenship may qualify for TN visa status under the USMCA, which covers certain computer-related occupations.
How to find solutions architect visa sponsorship jobs in Pennsylvania?
Migrate Mate filters job listings specifically by visa sponsorship willingness, so you can search solutions architect roles in Pennsylvania without sorting through positions that won't consider international candidates. The platform surfaces employers who have an active history of sponsoring technical roles in the state, which is particularly useful for targeting Philadelphia's financial technology sector and Pittsburgh's expanding enterprise IT market.
Which cities in Pennsylvania have the most solutions architect sponsorship jobs?
Philadelphia accounts for the largest share of solutions architect openings in Pennsylvania, driven by major financial institutions, healthcare systems like Jefferson Health and Penn Medicine, and large consulting firm offices. Pittsburgh is a growing secondary market, supported by Carnegie Mellon University's technology ecosystem, Google's local engineering presence, and PNC's headquarter operations. Malvern and the broader Philadelphia suburbs also see meaningful activity from firms in the insurance and financial services space.
Are there any Pennsylvania-specific considerations for solutions architects seeking visa sponsorship?
Pennsylvania's prevailing wage requirements for H-1B petitions are set at the county level, and Philadelphia-area wage floors for solutions architect roles reflect that region's competitive technical hiring market. Candidates coming through university partnerships at Carnegie Mellon, Penn, or Drexel may already have relationships with employers who sponsor OPT extensions and H-1B transfers. Pennsylvania's dense concentration of healthcare and financial services clients also means many solutions architect roles involve regulated data environments, which can affect how employers define and document the specialty occupation requirement.
What is the prevailing wage for sponsored solutions architect jobs in Pennsylvania?
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.