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RegularLanguage Fluency: English (Required)
Work Shift:
1st shift (United States of America)### Please review the following job description:
\*\*\*This role is 5 days a week in the Atlanta or Charlotte Office\*\*\*
The Senior AI Security Engineer helps design, implement, test, and operate the controls that keep enterprise AI systems safe, governed, and production ready.
This role focuses on the security engineering foundations required for AI\-enabled applications, agents, prompt\-driven workflows, and tool\-integrated automations operating in a regulated enterprise environment.
This is a hands\-on engineering role within the Forge AI Security \& Governance model.
The engineer supports guardrail implementation, prompt\-injection defense, output filtering, monitoring, secure tool\-use boundaries, logging, detection content, and deployment\-readiness controls for AI\-enabled systems.
The work spans design, testing, automation, detection engineering, and operational support across the AI delivery lifecycle.
Daily work includes implementing security controls for AI and agentic systems, validating configurations, supporting adversarial test preparation, building monitoring logic, partnering with engineering to harden prompt and tool behaviors, documenting controls, and ensuring AI solutions meet enterprise safety, traceability, and governance requirements before and after deployment.ESSENTIAL DUTIES AND RESPONSIBILITIES
Following is a summary of the essential functions for this role. Other duties may be assigned as needed.
AI \& Cloud Security Engineering
- Engineer and deploy security controls for AI/ML and Generative AI systems, including model‑level, data‑level, and platform‑level protections.
- Implement AI guardrails and safety controls (e.g., prompt injection defenses, content safety filters, policy enforcement, model access controls).
- Support secure AI platform onboarding for internal teams, ensuring alignment with Truist AI Security Standards and Review Processes.
- Perform technical security assessments of AI systems and cloud‑hosted AI services.
Infrastructure as Code \& Automation
- Design and implement Infrastructure as Code (IaC) using Terraform and CloudFormation to deploy AI security controls consistently.
- Build and maintain CI/CD pipelines (GitLab) for security tooling, guardrails, and configuration‑as‑code.
- Automate operational workflows using Python and scripting to reduce manual security operations.
Cloud Platform Security
- Engineer secure, scalable cloud environments supporting AI workloads across AWS and Azure.
- Implement and integrate cloud security tooling (e.g., Wiz) to provide visibility and control over AI assets.
- Secure containerized and orchestrated workloads supporting AI pipelines (ECS, EKS, Kubernetes).
Collaboration \& Enablement
- Partner with AI platform teams, application engineers, cloud security, and governance stakeholders to embed security into AI delivery.
- Contribute to the evolution of enterprise AI security standards, patterns, and reference architectures.
- Support incident response, threat modeling, and remediation activities related to AI systems.
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 7 years of experience in security engineering or related cybersecurity roles.
- Deep specialized knowledge in cybersecurity principles, theories, and concepts.
- Proven experience in software development lifecycle security practices.
- Deep knowledge of threat modeling, security testing, and penetration testing.
- Experience implementing and managing complex information security technologies.
Technical Skills \& Emerging Skills Experience
- Strong hands‑on experience with Azure and/or AWS
- Infrastructure as Code experience with Terraform and CloudFormation.
- Experience building and managing CI/CD pipelines (GitLab).
- Experience implementing or operating cloud security tooling (e.g., Microsoft Purview, Sentinel, Wiz or equivalent).
- Experience securing AI/ML or Generative AI systems in production environments.
- Familiarity with AI‑specific security controls, such as:
+ Prompt injection mitigation
+ Content safety / moderation controls
+ Model access and usage restrictions
+ Secure data handling for AI pipelines
- Exposure to Azure and Azure‑hosted AI services.
- Experience working in regulated environments with strong risk and governance requirements.
Additional experience we seek:
- 3\+ years of experience in security engineering, cybersecurity operations, application security, or a closely related technical discipline.
- Hands\-on experience implementing technical controls for enterprise software, APIs, cloud\-native services, or automation workflows.
- Working knowledge of AI/LLM security concepts such as prompt injection, unsafe output handling, tool\-use abuse, sensitive data exposure, and control boundary enforcement.
- Experience with logging, alerting, monitoring, or detection content for identifying suspicious or policy\-violating behavior in applications or workflows.
- Understanding of access control, identity boundaries, secrets handling, secure integration design, and environment\-based deployment controls.
- Ability to work with engineering teams to translate security concerns into implementable guardrails, validations, and release controls.
- Strong written documentation and communication skills, especially for controls, findings, remediation evidence, and technical guidance.
- Experience operating within enterprise governance, security, and release\-management practices where evidence\-based deployment readiness matters.
PREFERRED QUALIFICATIONS
- Experience with AI or agentic security controls, prompt and output protection strategies, or security validation of LLM\-enabled features.
- Experience with Microsoft, Azure, Copilot / Copilot Studio, or AI\-enabled enterprise workflow platforms.
- Experience with adversarial testing, red teaming support, detection engineering, or misuse\-case validation for AI\-enabled systems.
- Experience in financial services, cybersecurity, regulated enterprise environments, or platforms with high audit and control requirements.
- Familiarity with secure tool\-calling patterns, API protections, model or prompt change validation, and runtime traceability for AI systems.
- Working knowledge of cloud\-native security patterns, telemetry analysis, and deployment gating for modern engineering teams.
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.*
EEO is the Law E\-Verify IER Right to Work
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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Truist AI Hiring
Truist has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Atlanta, GA, US, Charlotte, NC, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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