Job description

Job Description: The AI Engineer II independently develops and supports AI engineering capabilities that enable reusable, secure, and scalable AI-assisted software delivery. This role builds components of the AI Harness, agent frameworks, evaluation systems, and developer workflow tools, while partnering with engineering teams to translate practical delivery needs into reliable AI-enabled solutions. The AI Engineer II contributes to production-quality AI applications and helps improve the consistency, safety, and usefulness of AI-supported engineering workflows. Responsibilities: Develop AI Harness & Platform Capabilities Design, build, test, and maintain well-defined AI platform components, services, connectors, and internal tools. Implement model routing, prompt execution patterns, API integrations, and reusable utilities aligned to platform standards. Improve reliability, performance, observability, and usability of AI engineering tools. Support production operations for AI-enabled services, including troubleshooting, monitoring, and issue remediation. May be responsible for on-call rotation and front-line incident response for AI-enabled production services, including triage, escalation, and remediation. Build Reusable Agents, Skills & Connectors Develop reusable agents, skills, workflow components, and connectors that can be used across engineering teams. Implement agent behaviors, tool calls, structured workflows, and integration logic that support AI-assisted SDLC use cases. Apply standards for modularity, permissions, traceability, and maintainability. Partner with more senior engineers to refine shared libraries and reduce duplicate AI engineering work across teams. Implement Evaluation, Testing & LLMOps Workflows Build and maintain evaluation workflows for prompts, agents, tools, and AI-generated outputs. Develop automated tests, regression checks, quality benchmarks, and validation approaches for AI-enabled capabilities. Support versioning, release management, rollback readiness, and lifecycle management for prompts, agents, and AI components. Analyze model and agent behavior to identify quality, cost, latency, security, or reliability concerns. Apply AI Security & Governance Controls Embed approved logging, access control, data classification, and risk controls into AI-enabled workflows. Work with security and governance partners to address implementation requirements for responsible AI use. Validate that AI tools and agents are handling sensitive data appropriately and operating within defined guardrails. Support remediation of security, compliance, or audit findings related to AI engineering solutions. Partner with Engineering Teams on AI-Assisted Delivery Collaborate with software engineering teams to understand delivery pain points and identify AI-enabled workflow improvements. Build examples, templates, and golden-path patterns that accelerate adoption of shared AI engineering capabilities. Support onboarding, technical guidance, and issue resolution for engineering users of AI platform tools. Contribute to team standards, documentation, and communities of practice around AI-assisted development. Requirements: Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, or a related technical field, or equivalent work experience, with typically 5 years of experience in software engineering, AI engineering, ML engineering, platform engineering, automation, or developer productivity roles. Strong programming skills are required, preferably in Python and one or more modern application development languages. Candidates should have experience building APIs, services, integrations, automated tests, and cloud-based applications. Experience with LLM application patterns, prompt engineering, RAG, agent frameworks, LLMOps, evaluation techniques, or AI-assisted development tools is strongly preferred. Candidates should demonstrate sound engineering judgment, strong collaboration skills, and the ability to independently deliver reliable technical solutions in a secure environment. As a forward-thinking software provider, Computer Services, Inc. (CSI) helps community and regional financial institutions solve their customers’ needs through open and flexible technologies. In addition to its award-winning core banking platform, these include the latest in lending, digital banking, payments, financial crime prevention and cybersecurity. Building on its 60-year track record of personalized service, CSI is shaping the future of banking and empowering its customers to rival their competition. For more information about CSI, visit www.csiweb.com CSI provides rewarding and challenging career opportunities for our employees. When determining your pay, we consider various factors such as your skills, qualifications, experience and location. Along with a competitive salary, this position includes eligibility for incentive awards based on both individual and business performance. We also offer a comprehensive range of benefits. To learn more about our benefits, visit: Benefits Summary CSI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, physical and mental disability, marital status, veteran status, or any other characteristic protected by applicable law. If you need an accommodation during the recruitment process, please email us at recruiter csiweb.com and we will work with you to meet your accessibility needs. For applicants residing in California, please read Privacy Notice for California Residents | CSI (csiweb.com) Visa Sponsorship: We are unable to offer visa sponsorship for this position. Applicants must be authorized to work in the United States without the need for sponsorship now or in the future.

Requirements

Must-haves

Develop AI Harness & Platform Capabilities
Design, build, test, and maintain well-defined AI platform components, services, connectors, and internal tools.
Implement model routing, prompt execution patterns, API integrations, and reusable utilities aligned to platform standards.
Improve reliability, performance, observability, and usability of AI engineering tools.
Support production operations for AI-enabled services, including troubleshooting, monitoring, and issue remediation.
May be responsible for on-call rotation and front-line incident response for AI-enabled production services, including triage, escalation, and remediation.
Build Reusable Agents, Skills & Connectors
Develop reusable agents, skills, workflow components, and connectors that can be used across engineering teams.
Implement agent behaviors, tool calls, structured workflows, and integration logic that support AI-assisted SDLC use cases.
Apply standards for modularity, permissions, traceability, and maintainability.
Partner with more senior engineers to refine shared libraries and reduce duplicate AI engineering work across teams.
Implement Evaluation, Testing & LLMOps Workflows
Build and maintain evaluation workflows for prompts, agents, tools, and AI-generated outputs.
Develop automated tests, regression checks, quality benchmarks, and validation approaches for AI-enabled capabilities.
Support versioning, release management, rollback readiness, and lifecycle management for prompts, agents, and AI components.
Analyze model and agent behavior to identify quality, cost, latency, security, or reliability concerns.
Apply AI Security & Governance Controls
Embed approved logging, access control, data classification, and risk controls into AI-enabled workflows.
Work with security and governance partners to address implementation requirements for responsible AI use.
Validate that AI tools and agents are handling sensitive data appropriately and operating within defined guardrails.
Support remediation of security, compliance, or audit findings related to AI engineering solutions.
Partner with Engineering Teams on AI-Assisted Delivery
Collaborate with software engineering teams to understand delivery pain points and identify AI-enabled workflow improvements.

Nice-to-haves

Experience with LLM application patterns, prompt engineering, RAG, agent frameworks, LLMOps, evaluation techniques, or AI-assisted development tools is strongly preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapimlpythonllmaccessibility

Platform: workday
Effort: long