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Showing 5 of 3,855+ Architect jobs
Role: Lead Agentic AI Engineer / Architect
Location: Atlanta, GA - 5 days Onsite
Duration: Long term
ROLE OVERVIEW
We are seeking an experienced Lead Agentic AI Engineer to design, build, and scale agentic AI workflows for enterprise platforms, intelligent processes, and AI-driven client solutions. In this hands-on leadership role, you will architect multi-agent systems, integrate enterprise-grade LLM capabilities across Azure AI Foundry, OpenAI, and Anthropic Claude, and deliver production-ready AI solutions that meet the strict compliance and reliability standards of the insurance and financial services industry. This is a highly hands-on technical leadership role where you will influence architecture, engineering practices, platform direction, and delivery execution.
KEY RESPONSIBILITIES
Agentic Workflow Design & Development
- Architect and implement multi-agent systems using frameworks such as LangGraph, Semantic Kernel, AutoGen, or CrewAI
- Design agent orchestration patterns including task decomposition, tool use, context management, memory, and human-in-the-loop (HITL) flows
- Build reliable agentic pipelines handling document extraction, reasoning, routing, and structured output generation
- Implement emerging agentic protocols including MCP (Model Context Protocol), Agent-to-Agent (A2A), AG-UI, and CodeAct Code Interpreter patterns
- Design and evaluate agent skills, manage agent harnesses, and maintain agent capability registries
- Design AI solutions capable of leveraging multiple LLM ecosystems including Azure OpenAI, OpenAI, Anthropic Claude, and open-source models based on workload characteristics, governance requirements, and cost/performance considerations
Full-Stack AI Application Engineering
- Build full-stack AI-native applications using React with streaming agent interactions, AG-UI components, and HITL design patterns
- Implement real-time agent communication interfaces with streaming output, MCP elicitation flows, and event-driven notifications
- Design and expose REST APIs and webhook integrations for agent-to-system and system-to-system interactions
Azure AI Platform Engineering
- Deploy and manage AI workloads on Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, and AKS
- Design event-driven and serverless architectures leveraging Azure Functions, Event Grid, Service Bus, and Azure API Management
- Build scalable, resilient, cost-efficient cloud architectures aligned with Azure Solutions Architecture best practices
- Implement Infrastructure as Code (IaC) using Terraform; establish pipeline-as-code and policy-as-code practices across CI/CD workflows
- Containerize AI workloads using Docker and Kubernetes for portable, scalable deployment
LLM Integration & Enterprise Reliability
- Lead prompt engineering, evaluation, and optimization strategies for OpenAI GPT models, Anthropic Claude, and Azure-hosted models
- Implement RAG architectures using vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB) and design extensible, evolvable schema and ontology models
- Focus on making enterprise AI systems reliable, accurate, controllable, and production-ready — especially when working with LLMs like OpenAI GPT models or Anthropic Claude models
- Design guardrails, output validation layers, and hallucination mitigation patterns for high-stakes enterprise workflows
Data Architecture — Relational, NoSQL & Graph
- Design and work across relational databases (PostgreSQL, SQL Server), NoSQL stores (Cosmos DB, MongoDB), and graph databases for knowledge graph and ontology-driven AI use cases
- Model extensible, evolvable schemas and domain ontologies that support AI reasoning, entity resolution, and semantic retrieval
Security & Identity
- Implement enterprise-grade security across AI systems: OAuth 2.0, Azure IAM, role-based and fine-grained access control (FGAC), managed identities, and credentials management
- Apply Azure security policies, RBAC, and least-privilege principles to AI platform components and agentic workflows
- Ensure secure handling of credentials, API keys, and secrets using Azure Key Vault and secure secrets management practices
AI-Native Engineering Practices
- Drive AI-assisted software engineering practices across the SDLC using copilots, autonomous coding agents, spec-driven development, and reusable engineering skills
- Leverage coding agents effectively across all SDLC phases — from requirements and design through development, testing, and deployment
- Help establish AI fluency standards and engineering productivity patterns across teams
- Contribute to internal AI accelerators, engineering frameworks, and delivery automation capabilities
- Enable engineering teams to effectively collaborate with AI systems while maintaining quality, governance, and reliability
Enterprise Governance & Responsible AI
- Implement responsible AI controls including observability, auditability, security, prompt protection, PII handling, and human oversight mechanisms
- Design enterprise-safe AI systems with governance, compliance, and reliability considerations built in from the ground up
- Establish patterns for AI system transparency, explainability, and accountability in regulated industry contexts
Technical Leadership & Modern Delivery
- Define AI engineering standards, design patterns, and best practices across the engineering organization
- Lead architecture reviews, code reviews, and technical roadmap planning for AI platform capabilities
- Mentor mid-level and junior engineers; foster a culture of AI-native engineering excellence
- Operate effectively in fast-moving, iterative AI delivery environments where experimentation, rapid prototyping, and production hardening coexist
- Balance innovation speed with engineering rigor, scalability, and maintainability
- Communicate complex AI concepts clearly to both engineering and business stakeholders
- Engage confidently with enterprise clients, architecture teams, and delivery leadership to shape AI solution direction
REQUIRED QUALIFICATIONS
Agentic AI & LLM Engineering
- 7+ years of software engineering experience with 3+ years in AI/ML or LLM-based systems
- Hands-on experience building production-grade agentic or multi-agent AI workflows
- Proficiency with GenAI agentic frameworks: LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain
- Working knowledge of agentic protocols: MCP (Model Context Protocol), A2A (Agent-to-Agent), AG-UI, and CodeAct/Code Interpreter patterns
- Strong experience with context management strategies, agent skill design, agent evaluation, and agent harness construction
- Proficiency with OpenAI APIs (GPT-4o, function calling, Assistants API) and Anthropic Claude APIs
- RAG pipeline design: vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB), chunking, embedding, and retrieval strategies
- Ability to pivot across agentic framework approaches and managed agent platforms as the ecosystem evolves
Full-Stack & API Engineering
- Full-stack experience with React; ability to build streaming agent interaction UIs, AG-UI components, and HITL design patterns
- Strong REST API and webhook design and implementation skills
- Proficiency in Python (intermediate level) and TypeScript for AI application and backend development
Cloud, Infrastructure & Architecture
- Strong Azure platform experience: Azure AI Foundry, Azure OpenAI, Azure ML, AKS, Azure Functions, API Management, Event Grid, Service Bus
- Infrastructure as Code using Terraform; pipeline-as-code and policy-as-code practices in CI/CD workflows
- Proficiency with containers (Docker, Kubernetes) for scalable AI workload deployment
- Ability to design and implement scalable, resilient, cost-efficient architectures on Azure
- Event-driven architecture and serverless architecture design and implementation
- Azure Solutions Architecture understanding across compute, networking, storage, security, and AI tiers
Data & Schema Design
- Experience with relational databases (PostgreSQL, SQL Server), NoSQL (Cosmos DB, MongoDB), and graph databases
- Ability to design extensible, evolvable schemas and domain ontologies that support AI reasoning and semantic retrieval
Security & Identity
- OAuth 2.0 implementation and Azure IAM/RBAC: permissions, policies, managed identities, and fine-grained access control (FGAC)
- Secure credentials management using Azure Key Vault and secrets management best practices
- Security-first mindset for AI systems: prompt protection, PII handling, data boundary enforcement
Engineering Practices
- Demonstrated ability to leverage coding agents and spec-driven development across all SDLC phases
- Strong GitHub Copilot and AI-assisted development tooling proficiency
- Experience leading technical teams and influencing engineering practices at an organizational level
NICE TO HAVE
- Experience designing and deploying Engineering Agent Skills that work alongside domain SMEs in human-AI collaborative workflows
- Wholesale insurance domain understanding: submission processing, broker/carrier workflows, market access, and underwriting operations
- Hands-on experience transitioning from custom agentic frameworks to managed agent platforms (Azure AI Agent Service, OpenAI Assistants, etc.)
- Experience with Databricks, MLflow, or Azure Databricks for data and model pipelines
- Prior work on document intelligence platforms (OCR, extraction, classification, IDP pipelines)
- Azure certifications (AI-102, DP-100, AZ-305) or relevant cloud AI credentials
- Contributions to open-source AI frameworks or published technical writing
- Experience working in startup or high-growth engineering environments
- Passion for AI-native engineering transformation and modern software delivery practices
Role: Lead Agentic AI Engineer / Architect
Location: Atlanta, GA - 5 days Onsite
Duration: Long term
ROLE OVERVIEW
We are seeking an experienced Lead Agentic AI Engineer to design, build, and scale agentic AI workflows for enterprise platforms, intelligent processes, and AI-driven client solutions. In this hands-on leadership role, you will architect multi-agent systems, integrate enterprise-grade LLM capabilities across Azure AI Foundry, OpenAI, and Anthropic Claude, and deliver production-ready AI solutions that meet the strict compliance and reliability standards of the insurance and financial services industry. This is a highly hands-on technical leadership role where you will influence architecture, engineering practices, platform direction, and delivery execution.
KEY RESPONSIBILITIES
Agentic Workflow Design & Development
- Architect and implement multi-agent systems using frameworks such as LangGraph, Semantic Kernel, AutoGen, or CrewAI
- Design agent orchestration patterns including task decomposition, tool use, context management, memory, and human-in-the-loop (HITL) flows
- Build reliable agentic pipelines handling document extraction, reasoning, routing, and structured output generation
- Implement emerging agentic protocols including MCP (Model Context Protocol), Agent-to-Agent (A2A), AG-UI, and CodeAct Code Interpreter patterns
- Design and evaluate agent skills, manage agent harnesses, and maintain agent capability registries
- Design AI solutions capable of leveraging multiple LLM ecosystems including Azure OpenAI, OpenAI, Anthropic Claude, and open-source models based on workload characteristics, governance requirements, and cost/performance considerations
Full-Stack AI Application Engineering
- Build full-stack AI-native applications using React with streaming agent interactions, AG-UI components, and HITL design patterns
- Implement real-time agent communication interfaces with streaming output, MCP elicitation flows, and event-driven notifications
- Design and expose REST APIs and webhook integrations for agent-to-system and system-to-system interactions
Azure AI Platform Engineering
- Deploy and manage AI workloads on Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, and AKS
- Design event-driven and serverless architectures leveraging Azure Functions, Event Grid, Service Bus, and Azure API Management
- Build scalable, resilient, cost-efficient cloud architectures aligned with Azure Solutions Architecture best practices
- Implement Infrastructure as Code (IaC) using Terraform; establish pipeline-as-code and policy-as-code practices across CI/CD workflows
- Containerize AI workloads using Docker and Kubernetes for portable, scalable deployment
LLM Integration & Enterprise Reliability
- Lead prompt engineering, evaluation, and optimization strategies for OpenAI GPT models, Anthropic Claude, and Azure-hosted models
- Implement RAG architectures using vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB) and design extensible, evolvable schema and ontology models
- Focus on making enterprise AI systems reliable, accurate, controllable, and production-ready — especially when working with LLMs like OpenAI GPT models or Anthropic Claude models
- Design guardrails, output validation layers, and hallucination mitigation patterns for high-stakes enterprise workflows
Data Architecture — Relational, NoSQL & Graph
- Design and work across relational databases (PostgreSQL, SQL Server), NoSQL stores (Cosmos DB, MongoDB), and graph databases for knowledge graph and ontology-driven AI use cases
- Model extensible, evolvable schemas and domain ontologies that support AI reasoning, entity resolution, and semantic retrieval
Security & Identity
- Implement enterprise-grade security across AI systems: OAuth 2.0, Azure IAM, role-based and fine-grained access control (FGAC), managed identities, and credentials management
- Apply Azure security policies, RBAC, and least-privilege principles to AI platform components and agentic workflows
- Ensure secure handling of credentials, API keys, and secrets using Azure Key Vault and secure secrets management practices
AI-Native Engineering Practices
- Drive AI-assisted software engineering practices across the SDLC using copilots, autonomous coding agents, spec-driven development, and reusable engineering skills
- Leverage coding agents effectively across all SDLC phases — from requirements and design through development, testing, and deployment
- Help establish AI fluency standards and engineering productivity patterns across teams
- Contribute to internal AI accelerators, engineering frameworks, and delivery automation capabilities
- Enable engineering teams to effectively collaborate with AI systems while maintaining quality, governance, and reliability
Enterprise Governance & Responsible AI
- Implement responsible AI controls including observability, auditability, security, prompt protection, PII handling, and human oversight mechanisms
- Design enterprise-safe AI systems with governance, compliance, and reliability considerations built in from the ground up
- Establish patterns for AI system transparency, explainability, and accountability in regulated industry contexts
Technical Leadership & Modern Delivery
- Define AI engineering standards, design patterns, and best practices across the engineering organization
- Lead architecture reviews, code reviews, and technical roadmap planning for AI platform capabilities
- Mentor mid-level and junior engineers; foster a culture of AI-native engineering excellence
- Operate effectively in fast-moving, iterative AI delivery environments where experimentation, rapid prototyping, and production hardening coexist
- Balance innovation speed with engineering rigor, scalability, and maintainability
- Communicate complex AI concepts clearly to both engineering and business stakeholders
- Engage confidently with enterprise clients, architecture teams, and delivery leadership to shape AI solution direction
REQUIRED QUALIFICATIONS
Agentic AI & LLM Engineering
- 7+ years of software engineering experience with 3+ years in AI/ML or LLM-based systems
- Hands-on experience building production-grade agentic or multi-agent AI workflows
- Proficiency with GenAI agentic frameworks: LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain
- Working knowledge of agentic protocols: MCP (Model Context Protocol), A2A (Agent-to-Agent), AG-UI, and CodeAct/Code Interpreter patterns
- Strong experience with context management strategies, agent skill design, agent evaluation, and agent harness construction
- Proficiency with OpenAI APIs (GPT-4o, function calling, Assistants API) and Anthropic Claude APIs
- RAG pipeline design: vector databases (Azure AI Search, PostgreSQL pgvector, Cosmos DB), chunking, embedding, and retrieval strategies
- Ability to pivot across agentic framework approaches and managed agent platforms as the ecosystem evolves
Full-Stack & API Engineering
- Full-stack experience with React; ability to build streaming agent interaction UIs, AG-UI components, and HITL design patterns
- Strong REST API and webhook design and implementation skills
- Proficiency in Python (intermediate level) and TypeScript for AI application and backend development
Cloud, Infrastructure & Architecture
- Strong Azure platform experience: Azure AI Foundry, Azure OpenAI, Azure ML, AKS, Azure Functions, API Management, Event Grid, Service Bus
- Infrastructure as Code using Terraform; pipeline-as-code and policy-as-code practices in CI/CD workflows
- Proficiency with containers (Docker, Kubernetes) for scalable AI workload deployment
- Ability to design and implement scalable, resilient, cost-efficient architectures on Azure
- Event-driven architecture and serverless architecture design and implementation
- Azure Solutions Architecture understanding across compute, networking, storage, security, and AI tiers
Data & Schema Design
- Experience with relational databases (PostgreSQL, SQL Server), NoSQL (Cosmos DB, MongoDB), and graph databases
- Ability to design extensible, evolvable schemas and domain ontologies that support AI reasoning and semantic retrieval
Security & Identity
- OAuth 2.0 implementation and Azure IAM/RBAC: permissions, policies, managed identities, and fine-grained access control (FGAC)
- Secure credentials management using Azure Key Vault and secrets management best practices
- Security-first mindset for AI systems: prompt protection, PII handling, data boundary enforcement
Engineering Practices
- Demonstrated ability to leverage coding agents and spec-driven development across all SDLC phases
- Strong GitHub Copilot and AI-assisted development tooling proficiency
- Experience leading technical teams and influencing engineering practices at an organizational level
NICE TO HAVE
- Experience designing and deploying Engineering Agent Skills that work alongside domain SMEs in human-AI collaborative workflows
- Wholesale insurance domain understanding: submission processing, broker/carrier workflows, market access, and underwriting operations
- Hands-on experience transitioning from custom agentic frameworks to managed agent platforms (Azure AI Agent Service, OpenAI Assistants, etc.)
- Experience with Databricks, MLflow, or Azure Databricks for data and model pipelines
- Prior work on document intelligence platforms (OCR, extraction, classification, IDP pipelines)
- Azure certifications (AI-102, DP-100, AZ-305) or relevant cloud AI credentials
- Contributions to open-source AI frameworks or published technical writing
- Experience working in startup or high-growth engineering environments
- Passion for AI-native engineering transformation and modern software delivery practices
Architect Jobs by Experience Level
Top Cities Hiring Architects
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Find Architect JobsArchitect Job Market
Who's Hiring
- Ryder System143
- BV Teck89
- Amazon Web Services69
- Capgemini58
- Cognizant50
Top Industries Hiring
- Technology & Software305
- Consulting & Professional Services39
- Construction & Real Estate23
- Electronics & Hardware19
- Insurance15
What Employers Look For
The qualifications that appear most often in architect jobs.
- Bachelor's or Master of Architecture degree from an accredited program
- Proficiency in Revit and AutoCAD for design documentation and coordination
- Active architectural license or documented progress toward ARE completion
- Experience across multiple project phases from schematic design through construction administration
- LEED accreditation or demonstrated familiarity with sustainable design principles
- Strong verbal and written communication skills for client presentations and consultant coordination
Tips for Your Architect Job Search
Tailor your portfolio to the firm type
Boutique residential firms and large commercial practices look for very different work samples. Lead with two or three projects that match the firm's focus, and annotate each one with your specific role, the program size, and the delivery phase you owned.
List your licensure status precisely
Many postings filter candidates by licensure. State whether you are a registered architect, an architectural intern, or ARE-in-progress, and list every state where you hold a license. Ambiguity here gets resumes skipped before a human reads them.
Apply early to roles that fit
Migrate Mate lists architect openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Match your software stack to the posting
Revit, AutoCAD, Rhino, SketchUp, and Enscape each signal different project types. Read the required and preferred tools in the job description and mirror that language in your resume skills section, using the exact product names the firm uses.
Prepare a project narrative for interviews
Interviewers at architecture firms ask you to walk through a project from brief to construction administration. Practice a two-minute version that covers the design challenge, your decision-making process, any value-engineering moments, and the built outcome.
Negotiate scope before you negotiate salary
Project type, delivery method, and client mix shape your long-term career more than starting pay. Before accepting an offer, ask which projects you would own, what the firm's BIM standards look like, and whether you would have client-facing responsibility in the first year.
Architect Jobs: Frequently Asked Questions
Which companies are hiring the most architects?
The companies hiring the most architects right now include Ryder System, BV Teck, and Amazon Web Services, with the largest share of openings in Texas, California, and Virginia, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in firms with active commercial, healthcare, and public-sector project pipelines.
How many architect jobs are remote?
About 74% of architect openings are fully remote or hybrid as of August 2026, with most of those positions skewed toward project management, specifications writing, and design review roles rather than production-heavy or construction-administration positions that require on-site presence.
How do you become an architect?
You earn an accredited professional architecture degree, complete the required hours of documented work experience through the Architectural Experience Program, and pass all sections of the Architect Registration Examination. Once you pass all divisions, you apply for licensure in the state where you plan to practice. Each state sets its own application requirements, so verify the specifics with that state's licensing board before you submit.
How do you get hired as an architect with little or no experience?
Focus on internship roles, junior designer positions, and firms that explicitly hire new graduates into structured project teams. Lead with academic work and any independent or competition projects in your portfolio. Emphasize software proficiency and the phases you contributed to in studio or internship settings. Smaller firms and design-build companies often give early-career architects broader exposure than large corporate practices.
What does the architect interview process look like?
Most architecture firms run two to three rounds. The first is usually a phone or video screen with HR or a project manager covering your background and availability. The second is a portfolio review with senior staff where you walk through selected projects in depth. A final round often involves meeting principals or partners and may include a brief design exercise or discussion of your approach to a specific project type the firm handles.
Where can I find and apply to architect jobs?
You can find and apply to architect jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your experience level, specialization, and preferred location, then apply directly to each one that fits.
See All 3,855+ Architect Jobs
Find roles that match your experience and apply in just a few clicks.
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