Security AI DevSecOps Engineer

Take your career to the next level! In the last few years our goal has been expansion, creating growth opportunities for many of our team members. Not only are we serious about growth, but we are also serious about helping our customers during hard financial times.

We take pride in providing solutions and offering a helping hand, not only to our customers but also to the communities we serve. As we continue to expand and grow into a national leader in consumer financing, we invite you to consider joining our team.

If you’re passionate about making a meaningful impact in people’s lives and bringing a personal touch to finance, we’d love to have you on board!

The AI Security Dev Sec Ops  Engineer is a specialized individual contributor role at the intersection of Dev Sec Ops , AI/ML security, and internal AI development. This person will embed with development teams across the organization to secure CI/CD pipelines and AI development life cycles end-to-end. In addition to securing how software and AI systems are built and deployed, this engineer will serve as our internal AI developer — designing, building, and orchestrating AI agents that transform how the Security team operates.

The ideal candidate is equally comfortable writing a pipeline security gate and building an LLM-powered automation workflow.

• Design and implement security controls across CI/CD platforms (e.g., Git Hub Actions, Git Lab CI, Jenkins, Azure Dev Ops), including SAST, DAST, SCA, container image scanning, and secrets detection.
• Develop and maintain policy-as-code enforcement using tools such as Open Policy Agent (OPA), Sentinel, or Kyverno to automate security guardrails at pipeline stages.
• Establish and govern infrastructure-as-code (IaC) security reviews using tools such as Checkov, tfsec, or Bridgecrew across Terraform and Cloud Formation environments.
• Lead software supply chain security initiatives including SBOM generation (Syft, Grype, etc), artifact signing (Sigstore/Cosign, etc), and dependency governance.
• Implement code security standards and report on compliance to such standards
• Ensure API security is effective and consistent with best practices

• Partner with AI/ML engineering teams to perform threat modeling of LLM-powered applications, model training pipelines, and inference serving layers.
• Implement controls aligned to OWASP LLM Top 10 and MITRE ATLAS, including defenses against prompt injection, data exfiltration, model inversion, and adversarial inputs.
• Secure MLOps workflows including model registries, dataset pipelines, and deployment platforms (e.g., MLflow, Kubeflow, Sage Maker).
• Evaluate and advise on AI vendor security posture, third-party model integrations, and emerging AI supply chain risks.

• Design, build, and maintain internal AI agents and automation workflows that accelerate Security team operations — including alert triage, vulnerability enrichment, threat intelligence correlation, compliance evidence collection, and reporting.
• Leverage LLM APIs (e.g., Anthropic Claude, OpenAI) and orchestration frameworks (e.g., Lang Chain, Llama Index) to build reliable, guardrailed agentic systems.
• Establish best practices for responsible internal AI development, including prompt governance, output validation, and audit logging of agent actions.
• Identify and prioritize automation opportunities across the Security team, translating manual workflows into AI-assisted or fully automated pipelines.

• Serve as a trusted security partner — not a gatekeeper — embedded with development teams to champion secure-by-default patterns and reduce friction in the SDLC.
• Desi…