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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 AI Agent Engineering & DevSecOps Automation Engineer is a senior engineering professional responsible for designing, developing, and operationalizing AI agents that automate DevSecOps control, engineering, and execution functions across the enterprise. This role builds secure, scalable, and reusable agentic capabilities that improve software delivery, strengthen control execution, and reduce manual effort across engineering teams.
The engineer develops agentic workflows and platform integrations that support control validation, policy enforcement, evidence collection, pipeline execution, application security remediation, infrastructure automation, engineering support, and platform operations. The role integrates AI capabilities with enterprise DevSecOps platforms, APIs, event-driven services, automation frameworks, and observability tooling while applying secure development, human oversight, auditability, and policy-as-code practices.
Success in this role requires strong software engineering fundamentals, hands-on Python and API development, experience with modern AI and LLM frameworks, and practical knowledge of DevSecOps, platform engineering, automation, and regulated technology environments. The engineer translates complex engineering and control requirements into production-grade agents and services that are measurable, maintainable, observable, and safe to operate at enterprise scale.
- Please note that for this opportunity, Truist will not sponsor an applicant for work visa status or employment authorization, nor will we offer any immigration-related support for this position (including, but not limited to H-1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN-1 or TN-2, E-3, O-1, or future sponsorship for U.S. lawful permanent residence status.)
Please also note that candidates must be located in or willing to self-relocate to one of the following locations: Charlotte, NC; Raleigh, NC or Atlanta, GA. Truist has 'in office' requirements that must be honored.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.
- Designs, builds, and implements enterprise infrastructure technology platforms and systems across cloud, network, database, storage, platform, computing, or middleware domains.
- Applies automation, monitoring, and optimization techniques to ensure high availability and performance of infrastructure.
- Manages infrastructure engineering projects and processes aligned with organizational policies and regulatory requirements.
- Collaborates with cross-functional teams and stakeholders to integrate new technologies and continuously improve infrastructure standards and processes.
- Troubleshoots and resolves complex technical issues impacting infrastructure performance and reliability.
- Supports compliance with technology strategies, standards, and governance to mitigate risks and ensure regulatory adherence.
- Reviews and guides infrastructure designs, configurations, and procedures to support operational consistency and knowledge sharing.
- Provides technical guidance and direction to lower-level technical professionals.
- Develops technical solutions with deep analysis of infrastructure systems and leads infrastructure processes.
Qualifications
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 in Computer Science, Engineering, Information Systems, a related field, or equivalent education, training, and work-related experience.
- Minimum of 5 years of professional experience in infrastructure engineering.
- Strong knowledge of enterprise infrastructure technologies including cloud, network, database, storage, platform, computing, and middleware.
Preferred Qualifications
- Bachelor's degree and six or more years of experience in software engineering, platform engineering, DevSecOps, intelligent automation, or an equivalent combination of education and experience.
- Hands-on experience designing and developing AI agents, agentic workflows, copilots, or autonomous automation solutions using modern LLM and orchestration frameworks.
- Strong proficiency in Python and API-first software development, including production-grade services, automated testing, secure coding, and integration patterns.
- Experience with retrieval-augmented generation, tool use, structured outputs, prompt and context management, model evaluation, and safeguards for enterprise AI solutions.
- Experience integrating AI agents with GitLab CI/CD, Ansible Automation Platform, infrastructure-as-code, policy-as-code, application security tooling, artifact repositories, and engineering workflow platforms.
- Experience automating DevSecOps controls such as policy enforcement, security scanning, control validation, evidence collection, exception handling, and remediation workflows.
- Working knowledge of REST APIs, event-driven architectures, Git-based development, containers, Kubernetes or OpenShift, and cloud or on-premises platform environments.
- Experience implementing agent observability, audit logging, telemetry, evaluation metrics, human-in-the-loop controls, access controls, and operational monitoring using OpenTelemetry, Splunk, Grafana, or similar technologies.
- Ability to translate complex engineering, risk, and control requirements into reusable automation that is secure, explainable, maintainable, and suitable for a regulated enterprise environment.
- Experience working in a regulated financial services, healthcare, or government environment where compliance constraints directly shape technical architecture decisions.
- Strong problem-solving and communication skills with the ability to explain technical complexity in plain language and collaborate across engineering, cybersecurity, risk, architecture, and product 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.
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Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 9,653 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Truist, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $214,400 based on 180 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Truist AI Hiring
Truist has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span Charlotte, NC, US, Raleigh, NC. Compensation range: $360K - $360K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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