ZipRecruiter

Log In

1

Ai Integration Engineer Jobs in Minnesota (NOW HIRING)

Lightedge

Sr. AI Integration Engineer

Chaska, MN

$111K - $150K/yr

The Senior AI Integration Engineer plays a central role in building, integrating, deploying, and supporting AI-driven workflows across Lightedge's core operational and business systems. This position ...

Lightedge

Sr. AI Integration Engineer

Chaska, MN · Hybrid

Our owned and operated facilities, integrated DR solutions, and premium compliant cloud choices ... intelligence (AI) tools to support parts of the hiring process, such as reviewing applications ...

Blank Metal Inc

AI Engineer

Minneapolis, MN · On-site

$108K - $146K/yr

Our team of seasoned engineers, designers, and strategists work across industries to create AI ... You understand how to integrate LLM APIs and AI services into robust applications, architect ...

US Bank

Head of AI Platform Engineering

Minneapolis, MN

$187K - $242K/yr

Integrate AI into core banking systems and customer engagement platforms * Track and report on AI performance metrics (e.g., cost savings, customer satisfaction) Basic Qualifications * Significant ...

Ecolab

AI Solution Architect

Saint Paul, MN

$63.75 - $84/hr

... integration, or AI enabled solutions in production environments. * 5 years' experience serving as a Solution Architect, Lead Engineer, Technical Architect, or comparable technical leadership role.

next page

Showing results 1-20

All JobsAi Integration Engineer JobsAi Integration Engineer Jobs in Minnesota

Ai Integration Engineer information

See Minnesota salary details

$43.6K

$121.7K

$169.9K

How much do ai integration engineer jobs pay per year?

As of Jul 26, 2026, the average yearly pay for ai integration engineer in Minnesota is $121,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,900.00 and $137,100.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position such as an AI executive, senior machine learning engineer, or AI research director that offers a total compensation package including salary, bonuses, and stock options. These roles usually require advanced skills in AI, deep learning, and data science, along with extensive experience and often involve leadership responsibilities in innovative technology companies.

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 does an AI integration engineer do?

An AI integration engineer designs and implements systems that incorporate artificial intelligence into existing software or hardware platforms. They work with machine learning models, APIs, and data pipelines to ensure seamless integration, often using programming languages like Python and tools such as cloud services. Their role involves testing, troubleshooting, and optimizing AI components for reliable performance within broader applications.

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.

Are AI engineers highly paid?

AI integration engineers are generally well-paid due to the high demand for expertise in artificial intelligence, machine learning, and data analysis. Salaries vary based on experience, location, and industry, but they tend to be above average compared to many other engineering roles. Advanced skills in programming, neural networks, and cloud platforms can further increase earning potential.

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 engineer makes 500,000 a year?

Highly experienced AI Integration Engineers working in senior or specialized roles at large technology companies can earn salaries approaching or exceeding $500,000 annually, especially when including bonuses and stock options. Such compensation typically requires advanced skills in machine learning, software development, and cloud platforms, along with extensive industry experience and a strong track record of successful projects.

What are AI Integration Engineers?

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.
What are popular job titles related to Ai Integration Engineer jobs in Minnesota? For Ai Integration Engineer jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Ai Integration Engineer jobs in Minnesota look for? The top searched job categories for Ai Integration Engineer jobs in Minnesota are:
What cities in Minnesota are hiring for Ai Integration Engineer jobs? Cities in Minnesota with the most Ai Integration Engineer job openings:
Ai Integration Engineer jobs near you
Infographic showing various Ai Integration Engineer job openings in Minnesota as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $121,716 per year, or $58.5 per hour.
Sr. AI Integration Engineer

Sr. AI Integration Engineer

Lightedge

Chaska, MN

$111K - $150K/yr

Other

Posted 22 hours ago

Job description

LightEdge Solutions is developing the IT solutions that will propel businesses forward over the next 10 years. Using a combination of shared and private/dedicated platforms, LightEdge has been successful in offering businesses alternatives that streamline operations, improve reliability and reduce costs.
If you are passionate about creating real solutions that help businesses with cutting-edge technology, want to be challenged to think out of the box and be in a position where you can impact change on a daily basis, then LightEdge can offer you a dynamic corporate environment built on teamwork and personal responsibility.

The Senior AI Integration Engineer plays a central role in building, integrating, deploying, and supporting AI-driven workflows across Lightedge's core operational and business systems. This position is an execution partner to AI architecture leadership, helping translate agentic design concepts and approved business needs into production-ready AI agents, automations, and integrations that improve workflow execution, operational efficiency, and decision support.

The ideal candidate combines strong software engineering and systems integration skills with practical experience delivering AI-enabled workflows in business environments. This person must be comfortable working directly with stakeholders to gather requirements, refine scope, and iterate quickly, while also owning a backlog of business-facing AI workflows and serving as the primary technical owner for those workflows once they move into production.

Key Responsibilities
  • AI Agent, Harness & Workflow Development: Design, develop, and maintain production-grade AI agents, harnesses, workflows, services, and integrations that support internal business processes and cross-functional execution.
  • AI Agent & Workflow Lifecycle Management: Own the end-to-end lifecycle of AI agents and AI-driven workflows, from intake and requirements shaping through production readiness, deployment, monitoring, support, periodic review, and continuous improvement.
  • Stakeholder Partnership: Work directly with internal teams to gather requirements, refine scope, collect feedback, and translate business needs into practical AI-enabled workflow solutions.
  • Owned Backlog: Maintain and execute a backlog of high-value business-facing AI workflows and automations that align with Lightedge priorities across operations, support, sales, and other internal functions.
  • System Integration: Build and support integrations across enterprise systems such as ServiceNow, Salesforce, portals, APIs, middleware, and other workflow platforms used by the business.
  • Production Ownership: Serve as the primary technical owner for AI workflows once they move from prototype into production, unless and until a deliberate transition to another long-term owner is defined.
  • Operational Support: Provide first-level support for production AI workflows that support critical business processes, including monitoring, issue triage, defect resolution, incident coordination, and early-life stabilization.
  • Controls and Readiness: Implement the controls required for safe production adoption, including testing, evaluation, observability, secrets handling, approval steps, rollback planning, and change-management alignment.
  • Engineering Standards: Contribute to codebases, deployment pipelines, support practices, and implementation standards for AI-enabled workflow delivery.
  • Optimization: Monitor, evaluate, and optimize the accuracy, reliability, cost, and business effectiveness of deployed AI workflows and integrations.
  • Documentation: Maintain clear technical documentation, workflow diagrams, runbooks, support notes, and production-readiness artifacts for delivered solutions.
  • Cross-Functional Collaboration: Partner with AI architecture, Security, Compliance, IT, Operations, and platform teams to ensure AI workflows are secure, supportable, and aligned with governance requirements.
  • Communications: Regularly communicate delivery status, risks, support needs, and business impact to stakeholders and leadership.
  • Architecture Contribution: Contribute implementation insight and field feedback into broader AI design and governance decisions, while not serving as the primary owner of enterprise AI strategy or architecture.
Required Qualifications
  • Experience building and supporting production workflow integrations across enterprise systems such as ServiceNow, Salesforce, APIs, middleware, and related business platforms.
  • Experience taking prototypes, proofs of concept, or citizen-developed automations into governed production environments with appropriate controls, reliability, and supportability.
  • Experience supporting production applications, integrations, or automations with direct operational ownership responsibilities.
  • Working knowledge of security, access controls, logging, monitoring, and change-management practices for business-critical systems.
  • Strong proficiency in Python for AI integrations, workflow automation, and data processing.
  • Experience with one or more of JavaScript/TypeScript, Java, or C# for API and application development.
  • Experience building and supporting APIs, microservices, distributed systems, and CI/CD pipelines.
  • Experience translating business requirements into production-ready technical workflows and integrations.
  • Experience evaluating AI outputs, prompt behavior, workflow quality, and operational reliability before production release.
  • Experience deploying and supporting production systems in cloud environments such as AWS, Azure, or GCP.
  • Ability to work directly with stakeholders to gather requirements, define scope, and iterate based on feedback.
  • Excellent communication skills and ability to translate technical decisions into business impact.
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
  • 5+ years of relevant experience in software engineering, systems integration, workflow automation, or AI-enabled application delivery.
Preferred Qualifications
  • Experience integrating AI-enabled workflows into ServiceNow, Salesforce, or similar enterprise platforms.
  • Experience building LLM-based applications, agents, or workflow automations using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent tools.
  • Experience developing AI-powered assistants, copilots, or intelligent automation workflows used by business teams.
  • Experience with prompt engineering, evaluation, and tuning of generative AI systems in production contexts.
  • Experience with Kubernetes and container-based deployment models.
  • Experience with MCP-style integration patterns, orchestration layers, or agent-accessible tool frameworks.
  • Experience with SQL and working with operational or analytics data across enterprise platforms.
  • Familiarity with vector databases, RAG pipelines, and workflow patterns that combine structured system context with LLM reasoning.
  • Familiarity with observability, support runbooks, and incident response practices for automation or integration services.
  • Experience with enterprise LLM platforms including ChatGPT, Claude, Gemini, Copilot, or similar tools.
  • Previous experience owning a backlog of automation or AI workflow enhancements for internal business users.
Success Metrics
  • Timely delivery of production-ready AI workflows that move approved use cases from prototype into governed production.
  • Reduction in manual work, cycle time, or support effort through well-integrated AI automations.
  • Reliable operation and first-level support of AI workflows that support critical business processes.
  • Strong stakeholder adoption and satisfaction across the internal teams using delivered AI workflows.
  • Clear business ROI from deployed AI workflows and automations.
  • Growth of a prioritized backlog of business-facing AI workflows aligned to Lightedge objectives.
With over 20 years in business, LightEdge offers a full stack of best-in-class IT services delivering flexibility, security, and control. Our solutions include premier colocation across seven purpose-built data centers spanning Des Moines, IA, Kansas City, MO, Omaha, NE, Austin, TX, and Raleigh, NC, industry-leading private Infrastructure as a Service (IaaS) and cloud platforms, and the top global security and compliance measures. Our owned and operated facilities, integrated DR solutions, and premium compliant cloud choices make up a true Hybrid Cloud Solution Center. LightEdge annually undergoes third-party audits for ISO 20000-1, ISO 27001, HIPAA, PCI-DSS 3.2, and SSAE 18 SOC 1 Type II, SOC 2 Type II and SOC 3.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
apply for this job

Apply