Cloud AI Architect Jobs
Cloud AI Architect jobs are open across technology, financial services, healthcare, and defense, from mid-level to principal and staff engineer, with specializations in LLM integration, MLOps platform design, and multi-cloud AI infrastructure. Find a role that fits from the openings below and apply directly.
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Showing 5 of 257+ Cloud 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).
Cloud AI Architect Jobs by Experience Level
Top Cities Hiring Cloud AI Architects
Explore cloud AI architect openings in the cities hiring most right now.
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Find Cloud AI Architect JobsCloud AI Architect Job Market
Who's Hiring
- Booz Allen Hamilton12
- JPMorganChase10
- Citi4
- Leidos4L
- Amazon4
Top Industries Hiring
- Technology & Software20
- Education13
- Banking & Financial Services11
- Investment & Asset Management9
- Healthcare & Medical Services5
What Employers Look For
The qualifications that appear most often in cloud AI architect jobs.
- Five or more years of hands-on experience designing production cloud infrastructure on AWS, Azure, or Google Cloud
- Demonstrated experience building and deploying machine learning or generative AI workloads at enterprise scale
- Proficiency with MLOps tooling such as Kubeflow, MLflow, Vertex AI Pipelines, or SageMaker Pipelines
- Strong programming skills in Python and familiarity with model serving frameworks like TensorFlow Serving, TorchServe, or Triton
- Experience with containerization and orchestration using Docker and Kubernetes in a cloud-native environment
- Bachelor's degree in computer science, engineering, or a related technical field, or equivalent professional experience
Tips for Your Cloud AI Architect Job Search
Quantify your AI platform impact
Hiring managers for cloud ai architect roles want to see outcomes, not responsibilities. Replace vague resume lines with metrics that show scale: model latency improvements, cost reductions from optimized inference pipelines, or the number of production workloads you migrated to a managed AI service.
Earn a cloud-provider AI certification
AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, and Azure AI Engineer Associate are the three certifications that appear most frequently in cloud ai architect postings. Holding at least one signals hands-on platform fluency and moves your application past automated filters.
Apply early to roles that fit
Migrate Mate lists cloud ai architect openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings by architecture pattern
Search for specific terms like RAG pipeline, vector database, inference optimization, or AI gateway in job descriptions rather than just the title. Cloud ai architect roles vary widely in scope, and matching your application to the architecture pattern a team is building dramatically improves fit.
Prepare a whiteboard architecture walkthrough
Most cloud ai architect interviews include a live design exercise where you architect an end-to-end AI system under constraints. Practice narrating your decisions aloud, covering data ingestion, model serving, observability, and cost guardrails, because interviewers evaluate your reasoning as much as the final design.
Negotiate for infrastructure ownership scope
When evaluating an offer, clarify whether you own the full AI platform stack or operate within a pre-built internal developer platform. Scope of ownership affects your career trajectory and portfolio depth, so confirm it before accepting rather than discovering it on day one.
Cloud AI Architect Jobs: Frequently Asked Questions
Which companies are hiring the most cloud ai architects?
The companies hiring the most cloud ai architects right now include Booz Allen Hamilton, JPMorganChase, and Citi, with the largest share of openings in Virginia, Texas, and New York, based on current listings on Migrate Mate as of August 2026. Demand is concentrated at large cloud-native technology firms and enterprises undergoing AI platform modernization.
How many cloud ai architect jobs are remote?
About 47% of cloud ai architect openings are fully remote or hybrid as of August 2026, reflecting the infrastructure-as-code nature of the work. Roles focused on platform engineering, MLOps architecture, and AI governance tend to be the most remote-friendly, while positions requiring close collaboration with on-site data science teams more often require in-office presence.
How do you become a cloud ai architect?
Start by building deep proficiency on at least one major cloud provider, then layer in hands-on machine learning engineering experience through real projects or open-source contributions. Earn a recognized cloud AI certification to validate platform skills. Move into roles that give you ownership of production AI infrastructure, and progressively take on cross-functional design responsibilities that combine data pipelines, model serving, and security governance.
Can you get hired as a cloud ai architect without direct experience?
It's possible to break into cloud ai architect roles by transitioning from a strong cloud engineering or data engineering background. Build a portfolio that includes a deployed end-to-end AI application on a public cloud provider, document your architecture decisions, and target mid-market companies where architects wear broader hats. Staff-augmentation contracts and internal transfers from cloud infrastructure roles are common entry points.
What does the cloud ai architect interview process look like?
The process typically runs three to five rounds and includes a recruiter screen, a technical phone interview covering cloud services and ML fundamentals, a live system design session where you architect an AI platform under constraints, and a final panel with engineering leadership and product stakeholders. Some employers add a take-home design exercise or ask you to walk through a past architecture decision and its trade-offs.
Where can I find and apply to cloud ai architect jobs?
You can find and apply to cloud ai architect jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the listings to find roles that match your skills and experience level, then apply directly to each position that fits.
See All 257+ Cloud AI Architect Jobs
Find roles that match your experience and apply in just a few clicks.
Find Cloud AI Architect Jobs