Senior Enterprise Solutions Engineer – AI Enablement and Platform Integration

Job Description

A leading organization is seeking a Senior Enterprise Solutions Engineer to design, support, and enhance enterprise technology platforms that enable efficient operations, automation, and AI-powered productivity across the business.

This position combines enterprise systems engineering, platform integration, AI enablement, automation, identity management, and cloud technologies. The successful candidate will be responsible for improving platform reliability, implementing scalable integrations, optimizing operational processes, and supporting modern AI-driven solutions.

The ideal candidate has hands-on experience with enterprise applications, cloud-based platforms, identity services, APIs, automation frameworks, and AI-enabled technologies in production environments.

Responsibilities

Enterprise Platform Engineering

• Support, administer, and optimize enterprise technology platforms and services.
• Maintain and improve cloud-based business applications and collaboration tools.
• Manage integrations between enterprise systems to improve operational efficiency and data flow.
• Ensure platform reliability, availability, and operational health.

AI Enablement and Automation

• Implement and support AI-enabled productivity solutions and workflows.
• Configure and maintain AI platform integrations and connected services.
• Optimize AI-assisted processes through workflow improvements and automation.
• Evaluate and implement practical AI capabilities that improve internal operations.
• Support responsible adoption and operational governance of AI-enabled solutions.

Systems Integration and Identity Management

• Develop and maintain integrations using APIs and automation platforms.
• Support identity and access management solutions across enterprise environments.
• Manage authentication and authorization technologies including SSO, OAuth, SAML, and SCIM.
• Improve connectivity and interoperability between enterprise applications and services.

Reliability and Operational Excellence

• Identify recurring technical issues and implement long-term solutions.
• Improve monitoring, observability, documentation, and platform support processes.
• Participate in incident response, troubleshooting, root cause analysis, and service improvement efforts.
• Automate repetitive operational tasks to reduce manual effort and support requirements.
• Improve overall system performance, stability, and user experience.

Cross-Functional Collaboration

• Partner with engineering, security, infrastructure, and technology teams to deliver platform improvements.
• Support enterprise-wide initiatives involving automation, integrations, cloud technologies, and AI platforms.
• Provide technical guidance and recommendations for platform enhancements and operational improvements.
• Contribute to technical planning, solution design, and continuous improvement initiatives.

Required Experience and Skills

Enterprise Systems and Platforms

• 7+ years of experience supporting enterprise technology environments.
• Experience administering and integrating enterprise SaaS platforms.
• Strong knowledge of identity and access management concepts.
• Experience with single sign-on technologies and identity protocols including OAuth, SAML, and SCIM.
• Experience supporting enterprise collaboration and productivity platforms.
• Experience with cloud-based enterprise applications and services.

AI Enablement

• Hands-on experience implementing and supporting AI-enabled productivity tools.
• Experience supporting workflows that leverage large language model technologies.
• Experience with AI platform integrations and automation.
• Knowledge of prompt engineering and AI workflow optimization.
• Understanding of AI governance and operational best practices.

Technical Skills

• Experience working with REST APIs and JSON-based integrations.
• Scripting experience using Python, PowerShell, Bash, or similar technologies.
• Experience designing and supporting enterprise integrations.
• Experience administering Microsoft 365 and/or Google Workspace environments.
• Familiarity with AWS cloud services.
• Experience with monitoring, observability, and operational support tools.
• Experience supporting endpoint management and service management platforms.

Preferred Experience

• Experience with Okta, Entra ID, Ping Identity, or similar identity platforms.
• Experience supporting Slack Enterprise environments.
• Atlassian platform administration experience.
• Experience with ServiceNow.
• Knowledge of Terraform and infrastructure-as-code practices.
• Experience with low-code or no-code automation platforms.
• Familiarity with enterprise AI governance programs.
• Experience in healthcare technology, regulated industries, or compliance-driven environments.

Core Competencies

• Strong troubleshooting and problem-solving skills.
• Ability to identify root causes and implement sustainable solutions.
• Experience improving operational efficiency through automation and process optimization.
• Strong communication and cross-functional collaboration abilities.
• Ability to independently manage complex technical environments and platform initiatives.
• Focus on reliability, scalability, continuous improvement, and operational excellence.

FAQ

1. What does a Senior Enterprise Solutions Engineer focused on AI enablement and platform integration do?

A Senior Enterprise Solutions Engineer designs and integrates enterprise technology solutions that enable organizations to adopt AI capabilities at scale. The role connects business requirements with platform architecture, APIs, data systems, cloud services, and AI technologies to deliver secure and maintainable enterprise solutions.

2. How does this role support enterprise AI adoption?

The engineer identifies integration opportunities, evaluates technical requirements, and helps embed AI capabilities into existing business applications and workflows. This can include enabling intelligent automation, AI-assisted processes, enterprise search, document processing, and other AI-driven use cases.

3. What does platform integration involve in this position?

Platform integration involves connecting applications, data platforms, APIs, cloud services, identity systems, and AI capabilities so they can operate as part of a cohesive technology environment. The engineer designs integration patterns and resolves dependencies between enterprise systems.

4. How are APIs used in enterprise AI integrations?

APIs provide controlled communication between AI services, enterprise applications, databases, and third-party platforms. The engineer designs or consumes APIs to exchange data, trigger workflows, integrate AI services, and maintain reliable communication between distributed systems.

5. What role do cloud platforms play in this position?

Cloud platforms provide the infrastructure and managed services required to host applications, integrate data, run AI workloads, and scale enterprise solutions. Engineers may work across AWS, Azure, or Google Cloud depending on the organization’s architecture and technology strategy.

6. How does the role address security and governance for AI-enabled platforms?

The engineer incorporates authentication, authorization, encryption, data protection, logging, and secure integration practices into solution designs. AI-specific considerations may also include controlling access to models and data, protecting sensitive information, and supporting organizational governance requirements.

7. What challenges are common in enterprise AI platform integration?

Common challenges include integrating legacy systems, managing inconsistent data sources, coordinating multiple APIs and platforms, addressing security constraints, and ensuring AI solutions can scale reliably. Engineers must also balance rapid AI adoption with long-term architecture and maintainability.

8. What technical skills are important for this role?

Strong knowledge of enterprise architecture, APIs, cloud platforms, distributed systems, databases, integration patterns, security, and software engineering is important. Experience with AI services, machine learning platforms, LLM integrations, automation, and modern data architectures is especially valuable.