Senior AI Engineer

DataJobs; Kansas; Hybrid; USD 140,000 - 190,000; Full time

Job summary

Jack Henry is seeking a Senior AI Engineer to turn AI experiments into secure, enterprise-grade production capabilities in a Kansas hybrid setup.

You will collaborate across engineering, cloud infrastructure, cybersecurity, and AI enablement to operationalize AI prototypes and platform features scalable across the organization while meeting governance expectations.

Qualifications

• Minimum 6 years of technical experience in software development.
• Production ownership of AI systems (AI agents, features, MCP servers).
• Experience with a major cloud environment (GCP, AWS, or Azure).
• Unit testing and end-to-end automated testing experience.
• Kubernetes experience.
• Experience with large language models (LLM) and/or vector databases.
• Willingness to travel up to 10% for meetings and conferences.
• Ineligible for immigration sponsorship and support.

Responsibilities

• Lead system analysis and engineering work to bring AI proofs of concept into production-ready software.
• Provide technical and engineering support from code delivery through retirement.
• Develop, test, and review applications against business requirements and best practices.
• Build and modify code using best practices and agentic development workflows.
• Consider downstream impact of code changes for end users and internal teams.
• Review code produced by less experienced team members.
• Collaborate with quality, security and governance stakeholders to ensure timely delivery of high-quality products.
• Create technical documentation and participate in cross-functional meetings.
• Stay current with emerging AI technologies and trends and advocate improvements to development processes.
• Define and document what production-ready means for AI systems.

Skills

Production ownership

Cloud environments

Kubernetes

LLMs & vector databases

Testing & reliability

Ambiguity tolerance

Travel

Education

Bachelor's degree

Tools

Kubernetes

Terraform

GCP

AWS

Azure

Job description

Join Jack Henry as a Senior AI Engineer (Kansas, hybrid) and help turn AI experimentation into secure, reliable, enterprise-ready production capabilities. You will collaborate across engineering, cloud infrastructure, cybersecurity, and AI enablement to operationalize AI prototypes, agents, and platform features that can scale across the organization while meeting compliance and governance expectations.

What you’ll deliver

• Lead system analysis and engineering work that brings AI proofs of concept into production-ready software, including research when patterns are not yet established.
• Provide technical and engineering support for new and existing applications from code delivery through application retirement.
• Develop, test, and review applications against business requirements and industry best practices.
• Build and modify code using best practices, standard guidance, and agentic development workflows.
• Consider downstream impact of code changes for end users and internal teams.
• Review code produced by less experienced team members.
• Partner with quality and security stakeholders (cybersecurity, cloud infrastructure, and AI governance) to support timely delivery of high-quality products.
• Create required technical documentation and participate in cross-functional meetings and discussions.
• Stay current with emerging AI technologies and industry trends, recommending improvements to software development processes.
• Follow departmental and corporate standards, including helping define and document what “production-ready” means for AI systems.

Production reliability for AI systems

• Own quality through unit tests, integration tests, and evals that support reliability, security, and performance for software, especially AI systems and workflows.
• Debug and troubleshoot issues as they arise, including AI-specific failure modes such as quality drift, prompt regressions, provider outages, and cost spikes.
• Build and support production AI capabilities including AI Gateway services, MCP integrations, agent frameworks, self-hosted inference, and AI-powered applications.
• Develop testing, evaluation, monitoring, logging, and observability practices to keep AI systems dependable, scalable, and auditable in production.
• Improve AI performance through model evaluation, operational monitoring, quality assurance, and continuous improvement practices.

Collaboration and leadership

• Work across teams and lead critical tasks and deliverables independently, including setting and updating expectations for scope and timelines.
• Contribute to product architecture when needed.
• Mentor engineers, establish best practices for AI development, and help drive adoption of AI technologies and agentic development workflows across the organization.

Requirements

• 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.
• Kubernetes experience.
• Experience with large language models (LLM) and/or vector databases.
• Comfort with ambiguity and shifting priorities.
• Self-directed, able to push through roadblocks rather than wait.
• Ability to travel up to 10% to attend internal meetings, training, and/or professional conferences.
• Ineligible for immigration sponsorship and support.

Hybrid location

This position requires 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.