ZipRecruiter

Log In

1

Ai Integration Engineer Jobs in Arizona (NOW HIRING)

Globe Telecom, Inc.

AI Cloud Engineer

Globe, AZ

$53 - $70.75/hr

AI Cloud Engineer experienced in cloud platforms, primarily Google Cloud Platform (GCP) and Amazon ... AI Integration & Model Deployment * Work closely with AI teams to build and manage cloud-based ...

ClifyX

AI Prompt Engineer

Chandler, AZ · On-site

... Integrate copilots with enterprise APIs, workflows, and data sources. • Implement responsible AI ... of prompt engineering frameworks and design patterns. • Hands-on experience with RAG ...

Confiz Limited

Principal AI Engineer

Phoenix, AZ · On-site

$180 - $230/hr

Architect full-stack solutions that integrate AI models into web/enterprise applications ... Collaborate with data engineers, ML engineers, full-stack developers, and product owners to ...

AIAEC LLC

Senior Python Backend Engineer

Phoenix, AZ · On-site

AI Integration: Collaborate with ML engineers to deploy and scale AI models, specifically focusing on RAG (Retrieval-Augmented Generation) workflows. ? Infrastructure & DevOps: Own the lifecycle of ...

Deloitte

AI Engineer Consultant

Tempe, AZ · Hybrid

You will work with an AI Data Engineer (data ingestion, curation, governance, platform foundations) and a Lead AI Solutions Architect (end-to-end solution architecture, integration patterns, non ...

next page

Showing results 1-20

All JobsAi Integration Engineer JobsAi Integration Engineer Jobs in Arizona

Ai Integration Engineer information

See Arizona salary details

$41.5K

$115.8K

$161.7K

How much do ai integration engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for ai integration engineer in Arizona is $115,810.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,900.00 and $130,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by AI integration engineers when deploying machine learning models into existing business systems?

AI Integration Engineers often encounter challenges such as ensuring compatibility between machine learning models and legacy systems, managing data privacy and security, and optimizing model performance for real-time applications. They must also address issues related to model scalability and monitoring, as well as facilitate smooth collaboration between data science, IT, and business teams. Overcoming these challenges requires strong problem-solving skills, effective communication, and a deep understanding of both AI technologies and enterprise infrastructure.

What are the key skills and qualifications needed to thrive as an AI integration engineer, and why are they important?

To thrive as an AI Integration Engineer, you need a solid background in computer science, programming (Python, Java, or similar), and experience with AI/ML frameworks, often supported by a bachelor's degree in a related field. Familiarity with cloud platforms (such as AWS, Azure, or Google Cloud), API development, and tools like TensorFlow or PyTorch is typically required. Strong problem-solving abilities, collaboration, and clear communication are essential soft skills for bridging technical and business needs. These competencies ensure successful deployment and seamless integration of AI solutions into existing systems, driving innovation and business value.

What is the difference between Ai Integration Engineer vs Data Scientist?

AspectAi Integration EngineerData Scientist
Required CredentialsBachelor's in CS, Engineering, or related; certifications in AI/ML toolsBachelor's or higher in CS, Statistics, or related; advanced degrees common
Work EnvironmentDeveloping and deploying AI solutions, integrating AI APIs into applicationsAnalyzing data, building predictive models, interpreting complex datasets
Employer & Industry UsageTech companies, AI service providers, software firmsResearch institutions, tech companies, finance, healthcare

While both roles involve AI, the Ai Integration Engineer focuses on implementing and integrating AI solutions into applications, whereas the Data Scientist analyzes data to develop models and insights. The roles often overlap but differ mainly in their primary focus: deployment versus analysis.

What is an AI integration engineer?

AI Integration Engineers are professionals who specialize in implementing artificial intelligence solutions into existing systems, products, or workflows. They work closely with data scientists, software developers, and business teams to ensure that AI models and technologies are effectively deployed and seamlessly integrated. Their responsibilities often include customizing AI tools, developing APIs, ensuring data compatibility, and monitoring performance post-integration. These engineers play a crucial role in bridging the gap between AI research and practical business applications.

Are AI Integration Engineers highly paid?

AI Integration Engineers typically earn higher-than-average salaries due to their specialized skills in AI systems, programming, and data analysis. Compensation varies based on experience, location, and industry, but they are generally well-compensated compared to many other engineering roles.

What are popular job titles related to Ai Integration Engineer jobs in Arizona?

For Ai Integration Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Ai Integration Engineer jobs in Arizona look for?

The top searched job categories for Ai Integration Engineer jobs in Arizona are:

What cities in Arizona are hiring for Ai Integration Engineer jobs?

Cities in Arizona with the most Ai Integration Engineer job openings:

Ai Integration Engineer jobs near you
Infographic showing various Ai Integration Engineer job openings in Arizona as of August 2026, with employment types broken down into 79% Full Time, 18% Part Time, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $115,810 per year, or $55.7 per hour.

AWS AI Platform Engineer

Tata Consultancy Service Limited

Phoenix, AZ • On-site

$110K - $130K/yr

Full-time

Re-posted 22 days ago

Job description

Senior Engineer AWS AI Platform, RAG and Agentic AI
Experience
• 1015 years of experience in Cloud Engineering, Platform Engineering, or Enterprise Architecture
• 4+ years of experience designing and implementing AI/ML and Generative AI solutions
• 2+ years of hands-on experience building RAG systems and AI Agents
• Experience working in large enterprise or financial services environments is highly preferred
Role Summary
We are seeking a Senior Engineer AWS AI Platform & RAG Integration to serve as the technical bridge between the AWS Cloud Infrastructure team, Enterprise AI Platform team, Security, Networking, Data Engineering, and Application Development teams.
The Engineering Lead will drive the onboarding of AI use cases onto the enterprise AI platform by coordinating cloud infrastructure requirements, designing scalable AI integration patterns, and implementing Generative AI solutions using AWS native AI services.
This role combines technical leadership, solution architecture, hands-on engineering, and cross-functional coordination to accelerate enterprise AI adoption while ensuring scalability, security, governance, and operational excellence.
Key Responsibilities
AI Platform Integration
• Lead onboarding of business applications onto the enterprise AI platform
• Translate business and AI requirements into AWS infrastructure and platform capabilities
• Design reusable AI integration patterns and reference architectures
• Define enterprise standards for AI application integration
• Support multiple AI initiatives across business domains
RAG and Agentic AI Development
• Design and implement Retrieval-Augmented Generation (RAG) architectures
• Build AI agents and multi-agent workflows for enterprise use cases
• Design enterprise knowledge retrieval and semantic search solutions
• Develop reusable AI orchestration components and AI APIs
• Integrate enterprise data sources into AI knowledge bases
• Implement prompt engineering and context management strategies
AWS Cloud Platform Engineering
• Work with AWS Cloud Infrastructure teams to use AI to provision and configure AWS Cloud infrastructure
• Design cloud-native AI architectures using AWS managed services
• Support infrastructure automation and deployment pipelines
• Ensure high availability, scalability, and resilience of AI workloads
• Coordinate networking, IAM, security, storage, and compute requirements
Cross-Team Leadership
• Act as the primary technical liaison between:
o AWS Cloud Infrastructure teams
o AI Platform teams
o Security and IAM teams
o Networking teams
o Data Engineering teams
o Application Development teams
o Enterprise Architecture teams
• Lead technical workshops and architecture discussions
• Coordinate cross-functional delivery activities
• Mentor engineering teams adopting AI capabilities
AI Governance and Operational Excellence
• Ensure AI solutions comply with enterprise security and governance standards
• Design secure AI integration patterns
• Implement AI guardrails and Responsible AI controls
• Support AI evaluation, monitoring, and observability
• Drive AI platform best practices and reusable accelerators
Required Technical Skills
AWS Cloud: VPC, IAM, EC2, ECS, EKS, Lambda, S3, API Gateway, CloudWatch, CloudFormation, EventBridge, SNS/SQS, Step Functions, KMS, Secrets Manager, Terraform, Elasticsearch, Cost Analysis, Budgeting
AWS AI Services: Amazon Bedrock, SageMaker AI, Amazon Knowledge Bases, Amazon OpenSearch, Amazon Titan, Bedrock Agents, Bedrock Guardrails, Textract, Comprehend, Transcribe, Rekognition, Neptune
AI Technologies: RAG architecture, Vector databases, Embeddings, Vector Search, Sematic search, Prompt engineering, Context Engineering, Agentic AI, Multi-agent orchestration, MCP, LangChain, LangGraph, LlamaIndex, AI evaluation techniques, Hallucination Mitigation Techniques, AI governance, LLM Models (Anthropic)
Programming: Python, Java, REST APIs, SDK integration, Git, CI/CD, Claude Code
Data Skills: SQL, NoSQL, Document processing, Data chunking, Metadata management, Data ingestion pipelines
Leadership Skills: Executive communication, Cross-functional coordination, Technical leadership, Architecture governance, Stakeholder management
Preferred Qualifications
• Experience with enterprise AI platform implementation
• Experience in Banking or Financial Services
• Familiarity with Responsible AI and AI Governance frameworks
• Experience implementing secure AI solutions in regulated environments
• AWS Professional or Specialty Certifications
• Experience with DevSecOps and Platform Engineering practices
Salary Range- $110,000-$130,000 a year
#LI-SP3
#LI-VX1

Apply