Data and AI Architect Jobs
Data and AI Architect jobs are open across technology, financial services, healthcare, and retail, from mid-level to principal and staff engineer, with specializations in cloud data platforms, machine learning infrastructure, and enterprise AI strategy. Scan the live roles below and apply to whichever ones fit.
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Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs.
In this senior individual‑contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self‑service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands‑on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness.
Responsibilities
- Defines and evolves Karman's enterprise AI reference architecture.
- Establishes architectural patterns for model access, retrieval‑augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management.
- Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities.
- Evaluates external platforms and models based on security, performance, cost, and business needs.
- Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies.
- Builds proofs of concept, reference implementations, reusable components, and technical accelerators.
- Provides hands‑on support for complex AI use cases, including RAG solutions, agentic workflows, model‑integrated applications, and automation.
- Establishes reusable templates, patterns, guardrails, and documentation to enable governed self‑service.
- Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight.
- Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions.
Required Qualifications
- Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering.
- Recent experience designing and deploying production generative artificial intelligence (AI) solutions.
- Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies.
- Hands‑on experience integrating commercial AI models through APIs and building model‑enabled applications.
- Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration.
- Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open‑weight models, and modern inference platforms.
- Experience designing secure systems in regulated or high‑assurance environments.
- Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues.
- Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences.
Preferred Qualifications
- Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries.
- Experience with GCC High, sovereign cloud, restricted environments, or controlled‑data architectures.
- Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication 800‑171, Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001.
- Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems.
- Familiarity with graphics processing unit (GPU) infrastructure, self‑hosted models, fine‑tuning, and private inference environments.
- Relevant Microsoft, cloud, architecture, or security certifications.
This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees.
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off
- Health Savings Account (HSA) with company contribution
- Flexible Spending Accounts (FSA)
- Company‑paid life and ADdummyamp;D insurance
- Short‑ and long‑term disability coverage
- Tuition reimbursement
Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, or any other status protected by applicable federal, state, or local law.
View our Privacy Policy
Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs.
In this senior individual‑contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self‑service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands‑on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness.
Responsibilities
- Defines and evolves Karman's enterprise AI reference architecture.
- Establishes architectural patterns for model access, retrieval‑augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management.
- Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities.
- Evaluates external platforms and models based on security, performance, cost, and business needs.
- Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies.
- Builds proofs of concept, reference implementations, reusable components, and technical accelerators.
- Provides hands‑on support for complex AI use cases, including RAG solutions, agentic workflows, model‑integrated applications, and automation.
- Establishes reusable templates, patterns, guardrails, and documentation to enable governed self‑service.
- Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight.
- Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions.
Required Qualifications
- Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering.
- Recent experience designing and deploying production generative artificial intelligence (AI) solutions.
- Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies.
- Hands‑on experience integrating commercial AI models through APIs and building model‑enabled applications.
- Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration.
- Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open‑weight models, and modern inference platforms.
- Experience designing secure systems in regulated or high‑assurance environments.
- Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues.
- Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences.
Preferred Qualifications
- Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries.
- Experience with GCC High, sovereign cloud, restricted environments, or controlled‑data architectures.
- Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication 800‑171, Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001.
- Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems.
- Familiarity with graphics processing unit (GPU) infrastructure, self‑hosted models, fine‑tuning, and private inference environments.
- Relevant Microsoft, cloud, architecture, or security certifications.
This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees.
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off
- Health Savings Account (HSA) with company contribution
- Flexible Spending Accounts (FSA)
- Company‑paid life and ADdummyamp;D insurance
- Short‑ and long‑term disability coverage
- Tuition reimbursement
Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, or any other status protected by applicable federal, state, or local law.
View our Privacy Policy
Data and AI Architect Jobs by Experience Level
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Find Data and AI Architect JobsData and AI Architect Job Market
Who's Hiring
- Booz Allen Hamilton20
- JPMorganChase11
- Johns Hopkins University Applied Physics Laboratory9
- University of San Diego6
- Penn State University4
Top Industries Hiring
- Education
- Technology & Software
- Banking & Financial Services
- Healthcare & Medical Services
- Insurance
What Employers Look For
The qualifications that appear most often in data and AI architect jobs.
- Five or more years of experience designing large-scale data or ML platforms
- Hands-on proficiency with at least one major cloud platform such as AWS, Azure, or GCP
- Experience with modern data lakehouse or data mesh architectural patterns
- Familiarity with MLOps tooling including feature stores, model registries, and deployment pipelines
- Strong knowledge of data governance, lineage, and security frameworks at enterprise scale
- Bachelor's or master's degree in computer science, data engineering, or a related technical field
Tips for Your Data and AI Architect Job Search
Tailor your resume to architectural scope
Data and AI architect roles care about the scale and complexity of systems you have designed, not just the tools you used. Quantify data volumes, pipeline throughput, and the number of downstream teams your platforms served to show real architectural impact.
Highlight cross-domain design decisions
Hiring managers want evidence you have made build-versus-buy, batch-versus-streaming, or on-premises-versus-cloud tradeoffs. Describe a specific decision, the constraints you weighed, and the outcome rather than listing every platform you have touched.
Apply early to roles that fit
Migrate Mate lists data and ai architect openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by cloud platform stack
Many data and ai architect postings are written around a specific hyperscaler, whether AWS, Azure, or GCP. Matching your primary certification and project depth to the stack named in the job description meaningfully improves your interview callback rate.
Prepare a system design narrative
Expect at least one architecture whiteboard or design review round. Practice walking through a real system you designed end to end, covering ingestion, storage, transformation, serving, and governance, so you can answer follow-up questions confidently and without notes.
Negotiate scope alongside compensation
Data and ai architect offers often carry wide variance in team size, cloud budget authority, and ML platform ownership. Before accepting, clarify what infrastructure decisions you will own versus inherit, since scope shapes both your day-to-day work and your next career move.
Data and AI Architect Jobs: Frequently Asked Questions
Which companies are hiring the most data and ai architects?
The companies hiring the most data and ai architects right now include Booz Allen Hamilton, JPMorganChase, and Johns Hopkins University Applied Physics Laboratory, with the largest share of openings in Virginia, California, and New York, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated in financial services, cloud-native technology firms, and large healthcare systems building out AI infrastructure.
How many data and ai architect jobs are remote?
About 56% of data and ai architect openings are fully remote or hybrid as of November 2026, reflecting the seniority and independence that most of these roles require. Cloud platform architecture and ML infrastructure design tend to be the most remote-friendly sub-areas, while data governance and enterprise data strategy roles more often require some on-site presence.
How do you become a data and ai architect?
Start by building deep hands-on experience as a data engineer or machine learning engineer, then move into designing systems rather than only building them. Earn a cloud architect certification on your primary platform, contribute to cross-functional platform decisions at your current employer, and develop fluency in governance, security, and cost optimization so you can lead architectural conversations with both engineering and business stakeholders.
How do you get hired as a data and ai architect with little experience?
Target associate or junior architect titles at mid-size companies, where the scope is narrower and mentorship is more accessible than at large enterprises. Build a portfolio of end-to-end architecture diagrams from personal or open-source projects that demonstrate real design decisions. Earning a cloud data or ML specialty certification before applying signals seriousness and closes part of the experience gap in recruiter screens.
What does the data and ai architect interview process look like?
Most processes run across four to six rounds. A recruiter screen is followed by a technical phone interview covering data modeling or ML system design fundamentals. The core round is typically a live architecture whiteboard or take-home design exercise where you must walk through tradeoffs in detail. Final rounds usually include a panel with senior engineers or platform leads and a conversation with a hiring manager or VP focused on strategic thinking and stakeholder collaboration.
Where can I find and apply to data and ai architect jobs?
You can find and apply to data and ai architect jobs on Migrate Mate, which lists current openings from companies across the United States. Search for roles that match your cloud platform experience, seniority level, and industry preference, then apply directly to each listing that fits.
See All 316+ Data and AI Architect Jobs
Find roles that match your experience and apply in just a few clicks.
Find Data and AI Architect Jobs