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Entry Level Artificial Intelligence Architect Jobs

Long Finch Technologies

Artificial Intelligence Architect Java 21+, Spring Boot, Spring AI Microservices, event-driven architecture (Kafka, SQS/SNS) REST/GraphQL API design, gRPC Design patterns, Enterprise architecture and ...

Long Finch Technologies

Artificial Intelligence Architect Java 21+, Spring Boot, Spring AI Microservices, event-driven architecture (Kafka, SQS/SNS) REST/GraphQL API design, gRPC Design patterns, Enterprise architecture and ...

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Entry Level Artificial Intelligence Architect information

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$39.5K

$72.7K

$136K

How much do entry level artificial intelligence architect jobs pay per year?

As of Sep 14, 2026, the average yearly pay for entry level artificial intelligence architect in the United States is $72,702.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $76,000.00 per year, depending on experience, location, and employer.

What is the difference between Entry Level Artificial Intelligence Architect vs Data Scientist?

AspectEntry Level Artificial Intelligence ArchitectData Scientist
Required CredentialsBachelor's in CS, AI, or related field; foundational knowledge of AI/MLBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentDesigning AI systems, collaborating with engineers, developing prototypesAnalyzing data, building models, interpreting results for business insights
Employer & Industry UsageTech companies, startups, AI-focused firmsVarious industries including finance, healthcare, marketing

While both roles involve working with data and AI technologies, an Entry Level Artificial Intelligence Architect focuses on designing and developing AI systems, whereas a Data Scientist primarily analyzes data to generate insights. The AI Architect role emphasizes system architecture and implementation, often requiring knowledge of AI frameworks, while Data Scientists focus on statistical analysis and modeling. Both roles are entry-level and share foundational skills, but their core responsibilities differ based on system design versus data analysis.

How do I become an entry level artificial intelligence architect?

To become an entry-level artificial intelligence architect, you typically need a bachelor's degree in computer science, data science, or a related field, along with knowledge of programming languages like Python and experience with machine learning frameworks such as TensorFlow or PyTorch. Gaining hands-on experience through internships, projects, or certifications in AI and machine learning can also help build relevant skills for this role.

How much do entry level artificial intelligence architects make?

Entry-level artificial intelligence architects typically earn between $70,000 and $100,000 annually, depending on location, education, and industry. Starting salaries may increase with proficiency in programming languages like Python, experience with machine learning frameworks, and relevant certifications.
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Entry Level Artificial Intelligence Architect jobs near you
Infographic showing various Entry Level Artificial Intelligence Architect job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 89% Full Time, 8% Part Time, and 2% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $72,702 per year, or $35 per hour.

Artificial Intelligence Architect

Augusta, MO • On-site

Long Finch Technologies
IT Services • 51 - 200 employees
Learn more about Long Finch Technologiesexternal

Full-time

Posted 17 days ago

Job description

NEED ONLY USC/GC. CAN WORK FROM ANY NEARBY OFFICE. NO NEED TO RELOCATE TO A DIFFERENT PLACE

WE ARE LOOKING FOR ARCHITECT FOR THIS POSITION NOT DEVELOPER.

Artificial Intelligence Architect

Java 21+, Spring Boot, Spring AI
Microservices, event-driven architecture (Kafka, SQS/SNS)
REST/GraphQL API design, gRPC
Design patterns, Enterprise architecture and scaling
AI / ML Integration
Experience with Agentic AI / multi-agent frameworks at enterprise scale.
LLM integration via Anthropic Claude API, OpenAI, or AWS Bedrock
RAG architectures, vector databases (OpenSearch, Pinecone, pgvector)
Model serving, inference optimization, prompt engineering
Spring AI, Langgraph, Google ADK, A2A, MCP.
Prompt Engineering, prompt caching, Token optimization.
AWS

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