Senior AI Engineer

DataJobs; Springfield (MO); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Outstanding benefit programs

Job summary

Jack Henry & Associates seeks a Senior AI Engineer in a hybrid Springfield, MO role to bridge AI experimentation with production engineering. You will deliver reliable, enterprise-ready AI systems, collaborating with security, quality, and architecture teams to ensure compliance and governance.

You will own production AI capabilities, lead production-grade development, and mentor engineers while advancing observability, testing, and documentation across projects.

Qualifications

• 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.
• Experience with unit testing and end-to-end automated testing.
• Experience with Kubernetes.
• Experience with at least one: Large language models (LLM), vector databases.
• Travel up to 10% for internal meetings.

Responsibilities

• Lead and move AI proofs of concept into production-ready software, including research when patterns are not established.
• Provide engineering support across the lifecycle from code delivery to application retirement.
• Develop, test, and review applications aligned to business requirements and best practices.
• Create technical documentation and participate in cross-functional discussions and meetings.
• Mentor engineers and drive adoption of AI technologies and agentic workflows.

Skills

Production ownership of AI systems

Cloud environments (GCP, AWS, Azure)

Kubernetes

LLMs

Vector databases

Unit testing

End-to-end testing

AI governance

Observability

Mentoring engineers

Education

Bachelor's degree

Tools

Terraform

AI Gateway services

Job description

Jack Henry & Associates is hiring a Senior AI Engineer for a hybrid role based in Springfield, MO (at least 1 day per week in an approved office location). This position focuses on bridging AI experimentation with production engineering so AI systems can be delivered as reliable, secure, enterprise-ready solutions, with attention to compliance and governance. You will collaborate across engineering, quality, security, and architecture to operationalize AI capabilities at scale.

What you’ll do

• Lead and perform system analysis and programming to move AI proofs of concept into production-ready software, including research when no established pattern exists.
• Provide engineering support for applications across the lifecycle, from code delivery through application retirement.
• Develop, test, and review applications aligned to business requirements and industry best practices.
• Use best practices, standard guidance, and agentic development workflows to create and modify code.
• Consider impacts of code changes on end users and internal teams, and review work produced by less experienced engineers.
• Partner with quality and security stakeholders (including cybersecurity, cloud infrastructure, and AI governance) to support timely delivery of high-quality products.
• Work independently on critical tasks by setting and updating expectations for scope, timelines, and delivery size.
• Create required technical documentation, and participate in cross-functional discussions and meetings.
• Stay current on emerging AI technologies and industry trends, recommending improvements to software development processes.
• Adhere to departmental and corporate standards, and help define what production-ready means for AI systems at Jack Henry.

• Lead unit tests, integration tests, and evals to improve reliability, security, and performance of developed 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.
• Contribute to product architecture when needed.
• Build and support production AI capabilities, including AI Gateway services, MCP integrations, agent frameworks, self-hosted inference, and AI-powered applications.
• Develop and improve testing, evaluation, monitoring, logging, and observability practices so AI systems remain dependable, scalable, and auditable in production.
• Improve AI system performance using model evaluation, operational monitoring, quality assurance, and continuous improvement.
• Mentor engineers, establish AI development best practices, and drive adoption of AI technologies and agentic workflows across the organization.

Minimum requirements

• 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.
• Experience with unit testing and end-to-end automated testing.
• Experience with Kubernetes.
• Experience with at least one: Large language models (LLM), vector databases.
• Comfort with ambiguity and shifting priorities.
• Self-directed style with willingness to push through roadblocks.
• Travel up to 10% to attend internal business meetings, training, and/or professional conferences.

Technologies

• GCP, AWS, Azure
• Kubernetes
• Large language models (LLM), vector databases
• AI Gateway services, MCP integrations, agent frameworks
• Self-hosted inference
• Unit testing, end-to-end automated testing
• Observability

Benefits

Outstanding benefit programs to ensure the physical, mental, and financial well-being of people is always met.

Location and work arrangement

This position is hybrid and requires at least 1 day per week in any of the following office locations:

• Allen, TX
• Louisville, KY
• Birmingham, AL
• Cedar Falls, IA
• Charlotte, NC
• Overland Park, KS
• Monett, MO
• Springfield, MO

Immigration

This position is ineligible for immigration sponsorship and support.

What would be 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.