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About This Role
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Regular or Temporary:
RegularLanguage Fluency: English (Required)
Work Shift:
1st shift (United States of America)### Please review the following job description:
- The Lead AI Security Engineer is a senior hands\-on security engineer responsible for designing, implementing, and advancing controls that protect AI\-enabled applications, agentic workloads, model integrations, and AI delivery pipelines across the full software and AI lifecycle.
- This role focuses on securing agent behavior, prompt and context flows, tool invocation, data access, model interaction, pipeline integrity, runtime execution, observability, and deployment readiness in a regulated enterprise environment.
- The engineer leads implementation of AI\-specific security patterns including prompt\-injection defenses, guardrails, output filtering, secure tool\-use boundaries, identity and permission controls, evidence capture, logging, monitoring, and detection content for AI\-enabled systems.
- The work spans architecture review, threat modeling, adversarial test readiness, control validation, automation, detection engineering, deployment gating, production monitoring, and incident response support for AI and agentic solutions.
- Daily work includes partnering with product, engineering, platform, data, risk, and security teams to translate AI security requirements into implementable controls that enable safe, traceable, resilient, and governed deployment of enterprise AI capabilities.
ESSENTIAL DUTIES AND RESPONSIBILITIES
Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.
- Lead the design and implementation of security controls for AI\-enabled applications, agents, model integrations, orchestration layers, and AI delivery pipelines.
- Perform AI and agentic threat modeling across prompts, context windows, retrieval flows, tools, APIs, permissions, memory, model access, data movement, and runtime execution paths.
- Implement and validate guardrails for prompt\-injection resistance, unsafe output handling, tool\-use abuse, sensitive data exposure, privilege escalation, model misuse, and policy\-violating behavior.
- Build and maintain monitoring, alerting, and detection logic for AI systems, including anomalous prompts, abnormal agent actions, suspicious tool invocation, unsafe model responses, and control degradation.
- Embed security requirements into AI design reviews, acceptance criteria, validation plans, CI/CD or LLMOps workflows, model or prompt change controls, and release\-readiness gates.
- Validate that AI solutions meet required security, governance, traceability, and evidence standards before release and continue to meet them after deployment.
- Support AI\-related incident investigation, root\-cause analysis, remediation planning, and operational response for suspicious behavior, control failures, data exposure, or unsafe system outcomes.
- Maintain control documentation, implementation guidance, runbooks, validation evidence, engineering patterns, and operating procedures for AI security engineering activities.
- Continuously improve AI security automation, validation workflows, detection content, guardrail logic, and deployment controls as models, agents, workflows, and attack techniques evolve.
Required Qualifications:
The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Bachelor’s degree or equivalent education, training, and work\-related experience.
- Minimum of 10 years of experience in security engineering or related cybersecurity roles.
- Deep specialized knowledge in cybersecurity principles, theories, and concepts.
- Extensive experience in software development lifecycle security practices.
- Expertise in threat modeling, security testing, and penetration testing.
- Proven experience implementing and managing complex information security technologies.
Additional Requirements:
- Minimum of 10 years of experience in security engineering, application security, product security, cloud security, cybersecurity operations, or related technical cybersecurity roles.
- Demonstrated experience leading complex security engineering efforts across modern software, API, cloud\-native, automation, or platform environments.
- Strong understanding of AI, LLM, or agentic security risks, including prompt injection, insecure tool use, data exposure, model misuse, pipeline compromise, and unsafe output handling.
- Experience with threat modeling, security testing, control validation, detection engineering, logging, monitoring, or incident response for production systems.
- Ability to translate security requirements into implementable engineering controls, validation criteria, deployment gates, documentation, and operational runbooks.
- 3\+ years of experience in a lead security engineering, application security, AI security, cybersecurity operations, or closely related technical discipline.
- Hands\-on experience implementing controls for enterprise software, APIs, cloud\-native services, workflow automation, model integrations, or agentic applications.
- Working knowledge of LLM and agentic security concepts such as prompt injection, indirect prompt injection, insecure tool use, excessive agency, sensitive data exposure, model misuse, and control boundary enforcement.
- Experience securing CI/CD, DevSecOps, MLOps, LLMOps, model, prompt, or configuration\-change pipelines through validation, approvals, evidence capture, and release controls.
- Experience with telemetry, logging, alerting, monitoring, or detection content for identifying suspicious, anomalous, or policy\-violating behavior in applications or AI workflows.
- Understanding of identity, access control, secrets handling, least privilege, secure integration design, API protections, sandboxing, and environment\-based deployment controls.
- Ability to partner with engineering teams to convert AI security risks into practical guardrails, tests, detections, monitoring requirements, and deployment\-readiness controls.
- Strong written documentation and communication skills, especially for control designs, validation results, remediation evidence, technical guidance, and audit\-ready operating procedures.
Preferred Qualifications:
- Experience securing AI agents, autonomous workflows, tool\-calling systems, retrieval\-augmented generation patterns, or LLM\-enabled enterprise applications.
- Experience with Microsoft, Azure, Copilot, Copilot Studio, Azure AI, or other enterprise AI and automation platforms.
- Familiarity with AI security guidance and frameworks such as OWASP LLM risks, OWASP agentic application risks, NIST AI RMF, MITRE ATLAS, or related industry practices.
- Experience with adversarial testing, AI red teaming support, misuse\-case validation, model or prompt evaluation, or safety monitoring for AI\-enabled systems.
- Experience in financial services, cybersecurity, regulated enterprise environments, or platforms with high audit, risk, privacy, and control expectations.
- Working knowledge of secure tool\-calling patterns, API protections, prompt and model change validation, runtime traceability, and observability for AI systems.
General Description of Available Benefits for Eligible Employees of Truist Financial Corporation: All regular teammates (not temporary or contingent workers) working 20 hours or more per week are eligible for benefits, though eligibility for specific benefits may be determined by the division of Truist offering the position. Truist offers medical, dental, vision, life insurance, disability, accidental death and dismemberment, tax\-preferred savings accounts, and a 401k plan to teammates. Teammates also receive no less than 10 days of vacation (prorated based on date of hire and by full\-time or part\-time status) during their first year of employment, along with 10 sick days (also prorated), and paid holidays. For more details on Truist’s generous benefit plans, please visit our Benefits site. Depending on the position and division, this job may also be eligible for Truist’s defined benefit pension plan, restricted stock units, and/or a deferred compensation plan. As you advance through the hiring process, you will also learn more about the specific benefits available for any non\-temporary position for which you apply, based on full\-time or part\-time status, position, and division of work.
*Truist is an Equal Opportunity Employer that does not discriminate on the basis of race, gender, color, religion, citizenship or national origin, age, sexual orientation, gender identity, disability, veteran status, or other classification protected by law. Truist is a Drug Free Workplace.*
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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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Truist, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Truist AI Hiring
Truist has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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