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Ai Integration Engineer Jobs in Fall River, MA (NOW HIRING)

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... chain, finance, engineering, and other functions to reduce manual work, improve access to ... AI, scripting, APIs, and integration tools for each use case. ROLE OBJECTIVE Deliver practical ...

Senior Software Engineer

Providence, RI · On-site +1

$123K - $162K/yr

The Senior Software Engineer designs, builds, and maintains the technical systems and application ... Lead the evaluation, integration, and responsible deployment of generative AI capabilities within ...

Senior Software Engineer

Providence, RI · On-site +1

$123K - $162K/yr

The Senior Software Engineer designs, builds, and maintains the technical systems and application ... Lead the evaluation, integration, and responsible deployment of generative AI capabilities within ...

Responsibilities : • Develop, integrate, test, and maintain software applications using modern AI-assisted software engineering workflows. • Utilize AI development tools such as Cursor, Claude ...

SEACORP is seeking to fill a Software Engineer - AI positionto support the development, integration, testing, and sustainment of modern USNavy software systems using advanced AI-assisted software ...

Develop, integrate, test, and maintain software applications using modern AI-assisted software engineering workflows. * Utilize AI development tools such as Cursor, Claude Code, OpenAI Codex ...

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Ai Integration Engineer information

See Fall River, MA salary details

$44.7K

$124.8K

$174.2K

How much do ai integration engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for ai integration engineer in Fall River, MA is $124,752.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,400.00 and $140,500.00 per year, depending on experience, location, and employer.

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.

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.

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 job categories do people searching Ai Integration Engineer jobs in Fall River, MA look for?

The top searched job categories for Ai Integration Engineer jobs in Fall River, MA are:

What cities near Fall River, MA are hiring for Ai Integration Engineer jobs?

Cities near Fall River, MA with the most Ai Integration Engineer job openings:

AI Solutions & Integration Engineer

Hope Global

Cumberland, RI • On-site

$92K - $124K/yr

Other

This job post has expired today. Applications are no longer accepted.


Job description

Hope Global is seeking a hands-on technology professional to turn business needs into secure, supportable automation and integration solutions. The role partners with IT and business teams across manufacturing, quality, supply chain, finance, engineering, and other functions to reduce manual work, improve access to information, and deliver measurable operational value.

This position is intentionally broad: the successful candidate does not need to arrive as an expert in AI, software-development, data management, or manufacturing platform. The expectation is strong enterprise technology fundamentals, sound judgment, the ability to learn quickly, and the ability to select the right combination of Microsoft cloud, Power Platform, AI, scripting, APIs, and integration tools for each use case.

ROLE OBJECTIVE Deliver practical, governed automation and integration solutions that improve time, cost, quality, service, or risk — and can be supported after launch.


Responsibilities

1. Business Process Discovery & Prioritization

· Partner with business and IT leaders to identify and prioritize automation, integration, analytics, and AI opportunities based on business impact, feasibility, risk, and supportability.

· Work directly with users, including plant-floor teams, to understand current workflows, pain points, data sources, controls, exceptions, and desired outcomes.

· Define a practical scope, success measures, and phased delivery approach before development begins.

2. Solution Design, Automation & Integration

· Design, build, configure, test, and support business solutions using Microsoft Power Platform, Microsoft 365, Azure services, approved AI capabilities, scripts, APIs, and enterprise connectors as appropriate.

· Integrate solutions with enterprise systems and data sources such as ERP, SharePoint, manufacturing applications, databases, files, and approved third-party platforms.

· Use PowerShell, Python, low-code tools, SQL, REST APIs, or other approved technologies where they provide the simplest maintainable solution.

· Build appropriate exception handling, logging, monitoring, approvals, and human review into production workflows.

3. Data, Analytics & AI Enablement

· Support data-driven solutions using Microsoft Fabric, OneLake, Power BI, SQL, relational data, and approved enterprise data models.

· Evaluate and implement appropriate AI-assisted capabilities, including Microsoft Copilot, Copilot Studio, Azure AI services, document intelligence, or approved large-language-model services when they provide clear business value.

· Use AI responsibly: validate outputs, define human checkpoints for business-critical decisions, and document known limitations.

4. Security, Governance & Supportability

· Design solutions using company standards for identity, role-based access, service accounts, credentials, secrets, data protection, privacy, and records retention.

· Maintain clear solution documentation, source/configuration control, testing evidence, support procedures, and change history.

· Follow established cybersecurity, change-management, release, and production-support practices.

5. Deployment, Adoption & Business Value

· Lead user acceptance testing, deployment, operational handoff, troubleshooting, maintenance, and continuous improvement of delivered solutions.

· Create concise documentation and training materials and help business users adopt new workflows successfully.

· Track reliability, usage, time savings, cost reduction, quality improvement, service improvement, or other agreed measures of value.

· Communicate status, risks, decisions, dependencies, and support needs clearly to technical and nontechnical stakeholders.


Required Qualifications

· Bachelor's degree in information systems, computer science, engineering, business technology, or a related field, or equivalent practical experience.

· Progressive experience in enterprise IT, systems engineering, cloud technology, systems integration, automation, application support, data platforms, or a related technical discipline.

· Strong experience with Microsoft enterprise technologies such as Azure, Microsoft 365, identity/access management, Power Platform, or related cloud services.

· Hands-on experience automating or integrating business processes using scripting, low-code tools, SQL, APIs, connectors, or comparable technologies.

· Working knowledge of data integration concepts, relational data, authentication/authorization, error handling, monitoring, and secure production support.

· Ability to learn unfamiliar platforms quickly and move from business problem to working technical solution with appropriate documentation and controls.

· Strong communication, analytical, and problem-solving skills, including the ability to work directly with business users and explain technical solutions clearly.

· Comfort working in manufacturing environments and managing multiple priorities with limited supervision.

Preferred Qualifications

· Experience with Microsoft Fabric, OneLake, Power BI, Power Apps, Power Automate, Copilot Studio, Azure AI services, or Azure Document Intelligence.

· Experience integrating ERP or manufacturing systems, particularly Oracle, Epicor, MES, QMS, CMMS, CRM, EDI, or similar platforms.

· Experience in automotive supply, discrete manufacturing, or another regulated/audited environment.

· Practical experience with Python, JavaScript, PowerShell, version control, or cloud-based development/integration services.

· Exposure to AI-assisted workflows, large-language-model APIs, agentic workflows, intelligent document processing, or related technologies.

· Familiarity with manufacturing quality/compliance processes or standards is helpful but not required.