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About This Role
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!
Job Purpose
The AI Security DevSecOps Engineer is a specialized individual contributor role at the intersection of DevSecOps, 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 lifecycles 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.
Duties and Responsibilities
CI/CD & Pipeline Security
- Design and implement security controls across CI/CD platforms (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps), 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 CloudFormation 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
AI/ML Security
- 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, SageMaker).
- Evaluate and advise on AI vendor security posture, third-party model integrations, and emerging AI supply chain risks.
Internal AI Development & Agent Orchestration
- 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., LangChain, LlamaIndex) 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.
Developer Partnership & Security Enablement
- 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.
- Design and deliver paved-road security templates, reusable pipeline components, and developer-facing runbooks that make the secure path the easy path.
- Facilitate threat modeling workshops and security design reviews for new products, services, and AI initiatives.
- Communicate security risks and recommendations effectively to both technical engineers and non-technical stakeholders.
Cloud & Secrets Management
- Partner with Development and Cloud teams to enhance secrets management strategy using tools such as HashiCorp Vault or AWS Secrets Manager, enforcing least-privilege access patterns.
- Monitor and improve cloud security posture (AWS, Azure, or GCP) including network security and container/Kubernetes hardening.
- Support incident response activities including those related to CI/CD, cloud, or AI-specific threats.
Metrics & Reporting
- Define and monitor key security metrics across pipeline security coverage, and AI agent operational health.
- Provide regular reporting on DevSecOps program maturity, AI security posture, and automation impact to security leadership.
Minimum Qualifications
Education & Experience
- Bachelor’s degree in Computer Science, Information Security, Information Technology, or a related field.
- 4–7 years of security engineering experience, with meaningful tenure in AppSec, DevSecOps, cloud security, or a closely related role.
- Demonstrated hands-on experience securing CI/CD pipelines using modern tooling (Snyk, Semgrep, Checkmarx, Trivy, Checkov, or comparable).
- Strong Python or similar scripting proficiency — this role builds tools, not just configures them.
- Working knowledge of LLM security risks including prompt injection, jailbreaking, model inversion, data poisoning, and AI supply chain threats.
- Experience building with LLM APIs or AI orchestration frameworks (LangChain, LlamaIndex, or similar).
- Cloud security proficiency in AWS, Azure, or GCP covering IAM, network security, secrets management, and container/Kubernetes hardening.
- Demonstrated ability to collaborate cross-functionally with engineering teams and translate security concepts for non-security audiences.
Preferred Qualifications
Education & Experience
- Master’s degree in Computer Science, Information Security, or a related field.
- Experience with MLOps platforms and securing model registries and training pipelines.
- Background in agentic AI architectures including multi-agent orchestration, tool use, memory management, and guardrail design.
- Experience with SIEM/SOAR platforms (Splunk, Exabeam, Tines, Torq, or similar) and security data pipelines.
Preferred Certifications
- Certified Information Systems Security Professional (CISSP)
- Offensive Security Certified Professional (OSCP) or Offensive Security Web Expert (OSWE)
- AWS Certified Security – Specialty or equivalent cloud security certification
- Certified AI Security Professional (CAISP) or comparable emerging AI security credential
- Certified DevSecOps Professional (CDP) or Certified DevSecOps Expert (CDE)
Critical Competencies
- Deep knowledge of DevSecOps principles, secure SDLC practices, and CI/CD security tooling.
- Strong understanding of AI/ML threat landscapes including OWASP LLM Top 10, MITRE ATLAS, and emerging attack vectors against AI systems.
- Ability to design, build, and operate AI agents and automation workflows in a production security context.
- Excellent written and oral communication skills with the ability to present to technical and executive audiences.
- Strong project management and organizational skills; able to manage multiple initiatives in a fast-paced, collaborative environment.
- Ability to work both collaboratively and independently, and to influence without direct authority.
- Intellectual curiosity and a growth mindset — the AI security landscape evolves rapidly and this role demands continuous learning.
Working Conditions
Hybrid work is permitted for this position. Regional has offices in Greenville, SC and Plano, TX available for in-person work. Some travel may be required (less than 10%).
#LI-hybrid
Regional is an equal opportunity employer and does not discriminate on the basis of race, color, religion, creed, national origin, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity, transgender status, age, disability, genetic information, veteran status, uniform service, or any other characteristic protected by applicable law (“Protected Characteristics”). Regional’s policy of non-discrimination applies to all phases of the employment process and relationship, including, but not limited to, recruitment and selection; compensation and benefits; professional development and training; promotions and opportunities; transfers; social and recreational programs; layoff; and terminations.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Regional Finance, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Regional Finance AI Hiring
Regional Finance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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