Senior AI Engineer

Hybrid opportunity in Kansas to help turn AI prototypes into secure, reliable, enterprise-ready capabilities. You will work across AI experimentation and production engineering, partnering with engineering, cloud infrastructure, cybersecurity, and AI enablement teams to ensure AI systems scale across Jack Henry while meeting compliance and governance needs.

What you’ll build and improve

You will bridge research-style problem solving with production delivery, operationalizing AI agents and platform capabilities into real applications. Work includes production AI capabilities such as AI Gateway services, MCP integrations, agent frameworks, self-hosted inference, and AI-powered applications, along with the engineering practices that keep these systems dependable and auditable.

Responsibilities

• Lead system analysis and programming to move AI proofs of concept into production-ready software, including research when no established patterns exist.
• Provide technical and engineering support for applications from code delivery through retirement of the application.
• Develop, test, and review applications based on business requirements and industry best practices.
• Use agentic development workflows and standard guidance to create and modify code.
• Consider how code changes impact end users and internal teams.
• Review code produced by less experienced engineers.
• Collaborate with quality and security partners (cybersecurity, cloud infrastructure, AI governance) to deliver high-quality products on time.
• Work cross-team as projects require, including leading critical tasks and deliverables with clear expectations on scope and timelines.
• Participate in cross-functional meetings and discussions and create required technical documentation.
• Stay current on emerging AI technologies and industry trends, recommending improvements to software development processes.
• Adhere to departmental and corporate standards, including participation in defining what “production-ready” means for AI systems.
• Lead unit tests, integration tests, and evals to support reliability, security, and performance of developed software, especially AI systems and workflows.
• Debug and troubleshoot issues, including AI-specific failure modes such as quality drift, prompt regressions, provider outages, and cost spikes.
• Contribute to product architecture as needed.
• Develop and support production testing, evaluation, monitoring, logging, and observability practices to keep AI systems dependable, scalable, and auditable.
• Partner with cybersecurity, cloud infrastructure, architecture, and business teams to design and deliver AI solutions that meet enterprise standards.
• Improve AI system performance using model evaluation, operational monitoring, quality assurance, and continuous improvement practices.
• Mentor engineers, establish best practices for AI development, and drive adoption of AI technologies and agentic workflows across the organization.
• Perform other duties as assigned.

Required qualifications

• Minimum 6 years of technical experience in software development.
• Production ownership of AI systems (AI agents, AI features in products, MCP servers, etc.).
• Experience with a major cloud environment: GCP, AWS, or Azure.
• Unit testing and end-to-end automated testing experience.
• Experience with Kubernetes.
• Experience with large language models (LLM) and/or vector databases.
• Comfort with ambiguity and shifting priorities.
• Self-directed approach and ability to push through roadblocks.
• Travel up to 10% for internal meetings, training, and/or professional conferences.
• Immigration sponsorship and support are not available for this position.

Hybrid location

Hybrid role requiring at least 1 day per week in one of the following office locations: Allen, TX; Louisville, KY; Birmingham, AL; Cedar Falls, IA; Charlotte, NC; Overland Park, KS; Monett, MO; Springfield, MO.

Technologies

• GCP, AWS, Azure
• Kubernetes
• Large language models (LLM)
• Vector databases
• MCP
• AI Gateway services
• Terraform

Nice to have

• Bachelor’s degree.
• Fintech experience.
• Experience with GCP and infrastructure as code (Terraform).
• Experience in cloud security and DevOps.
• Experience with model fine-tuning, evaluation (including controlling for bias), and validation.
• Experience with AI observability, evals, and model monitoring.