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About This Role
Location:
5501 Headquarters Dr, Plano, Texas, 75024, United States of America
AI Cybersecurity Engineer
Who We Are
At Upbound Group, we are committed to elevating financial opportunity for all through innovative, inclusive, and technology\-driven financial solutions that address the evolving needs and aspirations of consumers. The Company’s customer\-facing operating units include industry\-leading brands such as Rent\-A\-Center, Acima and Brigit that facilitate consumer transactions across a wide range of store\-based and digital retail channels, including over 2,400 company\-branded retail units across the United States, Mexico, New York and Puerto Rico. Upbound Group, Inc. is headquartered in Plano, Texas.
Role Summary
We are seeking a forward\-thinking AI Cybersecurity Engineer to join our Security team. This role sits at the convergence of Zero Trust architecture, Generative AI, agentic systems, and modern security engineering. The AI Cybersecurity Engineer will design, build, and operationalize next\-generation AI\-driven security capabilities \- including autonomous security agents, Retrieval\-Augmented Generation (RAG) pipelines, and Model Context Protocol (MCP) integrated toolchains \- to protect our infrastructure, data, and users against an ever\-evolving threat landscape. This role is critical to enabling safe, responsible AI adoption across our brands while maintaining the trust of the consumers we serve.
Key Responsibilities
- Apply Zero Trust principles to AI agents, ensuring agents operate under strict least\-privilege policies with scoped, time\-limited credentials.
- Secure GenAI deployments including LLM APIs, fine\-tuned models, and foundation model integrations against threats such as prompt injection, jailbreaking, training data poisoning, and model inversion attacks.
- Build and maintain guardrails, content moderation layers, and output validation pipelines for GenAI systems and LLMs used in security and business workflows.
- Conduct adversarial red\-teaming of GenAI systems, agent platforms, and LLMs to identify exploitable behaviors, unsafe outputs, and data exfiltration risks; develop remediation playbooks.
- Secure multi\-agent systems (MAS) that autonomously perform security tasks such as threat hunting, incident triage, vulnerability scanning, and policy enforcement.
- Define agent trust boundaries, inter\-agent communication security, and human\-in\-the\-loop (HITL) checkpoints to prevent runaway or adversarially hijacked agent behavior.
- Implement agent observability frameworks \- logging, tracing, and auditing all agent decisions, tool calls, and external API interactions for forensic and compliance purposes.
- Assess and mitigate agentic\-specific attack surfaces including goal hijacking, tool misuse, privilege escalation via chained tool calls, and unintended data exfiltration.
- Evaluate, harden, and govern the use of Model Context Protocol (MCP) servers that expose enterprise tools and data to AI agents \- treating each MCP server as a security boundary requiring authentication, authorization, and audit logging.
- Define and enforce MCP server access control policies, ensuring agents can only invoke permitted tools within approved scopes and that all MCP tool calls are logged and attributable.
- Assess MCP\-specific risks including prompt\-injected tool invocation, unauthorized resource access through MCP resource endpoints, and lateral movement via chained MCP server calls.
- Collaborate with platform and integration teams to establish secure MCP deployment standards, including mTLS for server communication, secrets management for server credentials, and rate limiting for tool invocations.
- Harden RAG pipelines against retrieval manipulation attacks, indirect prompt injection via poisoned knowledge base documents, and sensitive data leakage through retrieved context.
- Design RAG pipeline monitoring and anomaly detection to identify unusual retrieval patterns, high\-entropy queries indicative of extraction attacks, and drift in retrieved context quality.
- Build and deploy ML models for real\-time threat detection, behavioral anomaly detection, and user/entity behavior analytics (UEBA) across network, endpoint, and cloud telemetry.
- Develop LLM\-powered SOAR integrations that automate alert triage, root cause analysis, runbook execution, and stakeholder communication using natural language generation.
- Create GenAI\-assisted threat hunting workflows that allow analysts to query security data in natural language, with results grounded in live telemetry via RAG.
- Embed AI security controls (input validation, output filtering, adversarial testing) into CI/CD pipelines for AI systems alongside traditional DevSecOps practices.
- Contribute to AI Bill of Materials (AI\-BOM) tracking \- a comprehensive inventory of all deployed models, dependencies, training data sources, agents, MCP servers, and RAG pipelines \- to support supply chain security and compliance audits.
- Produce threat models, security architecture reviews, and risk assessments for AI\-enabled products; maintain living documentation as systems evolve.
- Integrate AI\-driven tools into daily engineering work to enhance decision\-making quality and accelerate innovation across deliverables.
Required Qualifications
- 5\+ years of experience in cybersecurity engineering, with at least 2 years of hands\-on experience in AI/ML security, GenAI systems, agentic platforms, and LLM application development.
- Deep understanding of Zero Trust architecture principles (NIST SP 800\-207\) and hands\-on experience implementing controls in cloud\-native or hybrid environments.
- Hands\-on experience with cloud security in at least one major cloud platform (AWS, Azure, or GCP), including cloud\-native IAM, Cloud Security Posture Management (CSPM), and cloud AI service security controls.
- Experience implementing Data Loss Prevention (DLP) controls within AI pipelines, including mechanisms to detect and block sensitive consumer data \- such as PII, SSNs, and payment card information from being transmitted to external LLMs or stored in AI system logs; familiarity with data residency requirements and privacy\-preserving techniques (e.g., tokenization, redaction) as applied to GenAI workflows.
- Demonstrated experience securing LLM\-based applications, including prompt injection defenses, output validation, and responsible AI guardrails.
- Hands\-on experience building or securing RAG pipelines, including vector database access control and retrieval\-layer security.
- Familiarity with agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI, or equivalent) and the security risks associated with autonomous multi\-agent systems.
- Strong Python proficiency; experience with ML frameworks (PyTorch, TensorFlow, Hugging Face transformers) and security data pipelines.
- Experience with SIEM/SOAR platforms (Rapid7, Microsoft Sentinel) and integrating AI capabilities into security operations workflows.
- Working knowledge of identity and access management (IAM), OAuth 2\.0 / OIDC, and secrets management (HashiCorp Vault, AWS Secrets Manager, Secret Server) in the context of AI system authentication.
- Familiarity with MITRE ATT\&CK, MITRE ATLAS (adversarial threats to AI/ML systems), and OWASP LLM Top 10\.
- Excellent communication skills; able to translate complex AI security risks for executive, legal, and non\-technical audiences.
- Experience developing and executing incident response procedures specific to AI systems, including response plans for model compromise, agent misbehavior events, and data exfiltration through LLM outputs.
- Demonstrated ability to author enforceable security policies and standards, including acceptable use frameworks, data classification guidelines, and AI security control baselines applicable across engineering and business teams.
Preferred Qualifications
- Hands\-on experience with Model Context Protocol (MCP) security controls in enterprise environments.
- Direct experience deploying or securing Claude Enterprise or a comparable enterprise AI assistant platform, including API security hardening, usage policy governance, role\-based access controls, and audit logging configuration.
- Experience conducting structured AI red\-teaming exercises against LLMs, RAG systems, or autonomous agents, including goal hijacking and tool misuse scenarios.
- Knowledge of adversarial ML (evasion, poisoning, model extraction) and model interpretability techniques (SHAP, LIME, attention visualization).
- Experience with AI governance, AI auditing, and compliance frameworks including NIST AI RMF, ISO 42001, or SOC 2 Type II for AI systems.
- Relevant certifications: CISSP, CISM, CEH, AWS/Azure/GCP Security Specialty, or emerging AI security credentials.
Work Location
Ability to work in the Plano, Texas office, Monday through Friday.
Sponsorship
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.
Equal Opportunity Employer
Upbound Group is an equal opportunity employer committed to ensuring all employment decisions are made on a non\-discriminatory basis in accordance with applicable federal, state, and local laws.
This job description is not intended to be all\-inclusive. Coworker may perform other related duties as negotiated to meet the ongoing needs of the organization.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Upbound Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Upbound Group AI Hiring
Upbound Group 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, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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