AI Architect Jobs
AI Architect jobs are open across technology, financial services, healthcare, and defense, from mid-level to principal and staff engineer, with specializations in machine learning infrastructure, large language model deployment, and enterprise AI platform design. Find a role that fits from the openings below and apply directly.
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Showing 5 of 921+ AI Architect jobs
Job Responsibilities:
- Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
- Write clean and efficient code, and ensure code maintainability and reusability.
- Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
- Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
- Perform code reviews to ensure code quality and consistency.
- Conduct testing to ensure application quality and reliability.
- Create and maintain technical documentation for web applications.
- Participate in project planning, estimation, and prioritization.
- Stay up to date with the latest technologies for Python development.
- Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
- Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
Requirement:
- Experience in software development in Python3.
- Decent understanding of the software development/testing life cycle.
- Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
- Experience with version control tools, such as Git.
- Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
- Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
- Solid knowledge of vector search concepts and at least one vector DB in production.
- Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.
Preferred Qualifications:
- Master’s Degree in Computer Science, Software Engineering, or related field.
- 1+ year professional experience in Python web application development with either Flask or Django.
- Experience in RESTful API development in Python.
- Understanding of Python web application frameworks such as Flask or Django.
- Experience with Cloud services, such as AWS.
- Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
- Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
- Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
- Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
- Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).
BeaconFire is an E-verified company and provides equal employment opportunities (visa sponsorship provided).
Job Responsibilities:
- Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
- Write clean and efficient code, and ensure code maintainability and reusability.
- Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
- Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
- Perform code reviews to ensure code quality and consistency.
- Conduct testing to ensure application quality and reliability.
- Create and maintain technical documentation for web applications.
- Participate in project planning, estimation, and prioritization.
- Stay up to date with the latest technologies for Python development.
- Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
- Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
Requirement:
- Experience in software development in Python3.
- Decent understanding of the software development/testing life cycle.
- Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
- Experience with version control tools, such as Git.
- Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
- Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
- Solid knowledge of vector search concepts and at least one vector DB in production.
- Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.
Preferred Qualifications:
- Master’s Degree in Computer Science, Software Engineering, or related field.
- 1+ year professional experience in Python web application development with either Flask or Django.
- Experience in RESTful API development in Python.
- Understanding of Python web application frameworks such as Flask or Django.
- Experience with Cloud services, such as AWS.
- Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
- Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
- Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
- Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
- Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).
BeaconFire is an E-verified company and provides equal employment opportunities (visa sponsorship provided).
AI Architect Jobs by Experience Level
Top Cities Hiring AI Architects
Explore AI architect openings in the cities hiring most right now.
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Find AI Architect JobsAI Architect Job Market
Who's Hiring
- Amazon Web Services42
- Boston Consulting30
- NVIDIA23
- Booz Allen Hamilton18
- CVS Health12
Top Industries Hiring
- Technology & Software144
- Consulting & Professional Services45
- Electronics & Hardware26
- Banking & Financial Services22
- Education15
What Employers Look For
The qualifications that appear most often in AI architect jobs.
- Bachelor's or master's degree in computer science, engineering, or a related technical field
- Hands-on experience designing and deploying large-scale machine learning or deep learning systems in production
- Proficiency with at least one major cloud AI platform such as AWS SageMaker, Azure AI, or Google Vertex AI
- Experience with MLOps tooling including model versioning, CI/CD pipelines, and model monitoring frameworks
- Strong programming skills in Python and familiarity with frameworks such as PyTorch, TensorFlow, or Hugging Face
- Demonstrated ability to translate business requirements into scalable AI system architecture and communicate trade-offs to non-technical stakeholders
Tips for Your AI Architect Job Search
Anchor your resume to system design
AI architect roles are evaluated on architecture decisions, not just model performance. Show how you designed end-to-end pipelines, chose infrastructure trade-offs, and made systems production-ready. Hiring managers want to see the reasoning behind your choices, not just the outcome.
Apply early to roles that fit
Migrate Mate lists ai architect openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Tailor your application to the stack listed
AI architect postings vary widely by cloud provider and tooling. A role built around Azure AI Studio reads differently than one centered on AWS SageMaker or Google Vertex AI. Reframe your experience around the specific platform named in the job description before you apply.
Translate research projects into production wins
Many candidates describe proof-of-concept work without framing it as production-scale impact. Restate your projects in terms of throughput, latency targets, uptime, or cost reduction. Interviewers want to know your systems ran under real load, not just in a notebook.
Prepare for a multi-round technical format
Expect separate rounds for system design, ML fundamentals, and a stakeholder or cross-functional presentation. Practice whiteboarding an architecture end-to-end, including data ingestion, model serving, monitoring, and rollback strategy, not just the modeling layer.
Negotiate scope and team structure, not just pay
AI architect offers often leave ownership boundaries ambiguous. Before you accept, clarify whether you own the full lifecycle from design through deployment, whether you have a dedicated platform team, and what the escalation path looks like for infrastructure decisions.
AI Architect Jobs: Frequently Asked Questions
Which companies are hiring the most ai architects?
The companies hiring the most ai architects right now include Amazon Web Services, Boston Consulting, and NVIDIA, with the largest share of openings in California, Texas, and Virginia, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in technology, financial services, and healthcare organizations scaling enterprise AI platforms.
How many ai architect jobs are remote?
About 60% of ai architect openings are fully remote or hybrid as of August 2026, making it one of the more flexible senior technical roles in the market. Sub-areas like LLM integration, cloud AI platform design, and ML platform engineering tend to have the highest share of remote-eligible postings.
How do you become an ai architect?
Start by building deep hands-on experience as a machine learning engineer or data engineer, then take on system design responsibilities that go beyond modeling. Develop fluency in cloud AI services, MLOps practices, and distributed systems. Work toward leading end-to-end architecture reviews, documenting design decisions, and presenting trade-offs to engineering and product leadership.
Can you get hired as an ai architect with little experience?
Breaking in is possible if you can demonstrate production-level thinking rather than just research experience. Build a public portfolio showing a complete system you designed, including data ingestion, model serving, and monitoring. Targeting roles titled ML platform engineer or senior ML engineer first gives you the production credibility that ai architect postings require.
What does the ai architect interview process look like?
Most processes run three to five rounds. Expect an initial screening call focused on your background and past architecture decisions, followed by a deep-dive system design round where you whiteboard an end-to-end AI pipeline. Later rounds often include an ML fundamentals review and a cross-functional presentation where you walk non-technical stakeholders through a proposed architecture.
Where can I find and apply to ai architect jobs?
You can find and apply to ai architect jobs on Migrate Mate, which lists current openings from companies across the United States. Search the listings to find roles that match your background, experience level, and preferred location, then apply directly to each one that fits.
See All 921+ AI Architect Jobs
Find roles that match your experience and apply in just a few clicks.
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