Sr. AI Integration Engineer Job Des Moines area,Iowa USA,Software Development

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 Service Now, 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 Service Now, 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/Type Script, 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 Service Now, Salesforce, or similar enterprise platforms.
Experience building LLM-based applications, agents, or workflow automations using frameworks such as Lang Chain, Llama Index, 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…