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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 Senior Director, Agentic Engineering serves as the executive leader of Forge's agentic engineering organization and is accountable for the technical vision, engineering execution, and operational excellence behind Truist's enterprise AI agent ecosystem. This leader owns the engineering function responsible for designing, building, deploying, and operating production-grade AI agents, multi-agent systems, and agentic workflows that transform how teammates and clients interact with the Firm.
This is a highly technical player-coach leadership role that combines executive organizational leadership with deep hands-on engineering expertise. The Senior Director leads the engineering function, establishes technical direction, drives work planning and execution, and serves as the ultimate escalation point for architecture and engineering decisions across the agentic engineering portfolio.
The successful candidate will bring extensive experience building large-scale software and AI systems, including expertise in Python, modern systems languages such as Go, agentic frameworks, LLM orchestration, retrieval architectures, and enterprise AI platform engineering. This leader will be responsible for shipping production agentic systems, not simply defining roadmaps, ensuring all solutions meet Truist standards for security, scalability, resiliency, observability, governance, and regulatory compliance.
The Senior Director partners closely with Product, Architecture, Security, Platform Engineering, Data Engineering, Risk, and executive leadership teams to translate business strategy into enterprise-scale AI capabilities while building and mentoring a world-class team of agentic engineers.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 Agentic Engineering function, providing executive leadership, organizational design, technical governance, delivery accountability, and talent leadership across all agentic engineering initiatives.
- Own the technical direction for Forge agentic solutions, establishing architecture standards, engineering patterns, and development practices for enterprise AI agents and multi-agent systems.
- Drive work planning, prioritization, execution management, and delivery outcomes across the agentic engineering organization, ensuring teams consistently deliver high-quality production systems aligned to business commitments.
- Serve as the final engineering authority for architecture decisions, engineering standards, and critical code review escalations across the agentic engineering portfolio.
- Design and govern enterprise patterns for agent orchestration, tool invocation, retrieval-augmented generation (RAG), memory management, prompt architecture, workflow automation, reasoning frameworks, evaluation pipelines, observability, and operational support.
- Lead the engineering strategy for enterprise agent platforms including Microsoft Copilot Studio, Azure AI Foundry, and other emerging agentic technologies required to support the Forge roadmap.
- Partner with Product, Technology, Security, Risk, Architecture, and Data leaders to ensure AI solutions are secure, compliant, scalable, and aligned to enterprise priorities.
- Build and mentor a high-performing engineering organization through hiring, performance management, succession planning, coaching, career development, and technical leadership.
- Establish and enforce engineering excellence standards including software craftsmanship, testing strategies, release management, architecture review, secure development practices, operational readiness, and reliability engineering.
- Drive engineering execution across complex cross-functional initiatives involving application engineering, platform engineering, data engineering, AI engineering, cyber security, and enterprise architecture teams.
11.Champion engineering rigor through measurable quality controls, system reliability objectives, operational metrics, and continuous improvement practices.
- Evaluate emerging AI technologies, frameworks, models, and engineering approaches, identifying opportunities to accelerate delivery, improve reliability, and strengthen enterprise capabilities
- Represent Agentic Engineering in executive governance forums, architecture review boards, risk committees, vendor engagements, regulatory discussions, and industry working groups.
- Manage organizational budgets, vendor relationships, workforce planning, engineering tooling investments, and resource allocation to support strategic objectives.
- Foster a culture of accountability, innovation, engineering excellence, continuous learning, and responsible AI development across the organization
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 or higher in Information Technology, Computer Science, Business Administration, a related field, or equivalent education, training and work-related experience.
- Minimum of 12 years of progressive experience in technology operations.
- Broad leadership and management experience leading complex technology operations teams.
- Proven ability to manage complex operational initiatives across multiple teams.
Preferred Qualifications
- Experience leading enterprise Agentic Engineering, AI Platform Engineering, Applied AI, Intelligent Automation, or Emerging Technology organizations.
- Deep expertise in multi-agent systems, agent orchestration frameworks, prompt engineering, reasoning architectures, retrieval-augmented generation (RAG), memory systems, agent evaluation, and human-in-the-loop workflows.
- Experience building production AI applications using Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, Semantic Kernel, LangGraph, CrewAI, OpenAI Agents SDK, Amazon Bedrock AgentCore, Google Vertex AI Agent Engine, or similar platforms.
- Experience operating enterprise-scale AI platforms supporting thousands of users and mission-critical business processes.
- Strong understanding of AI governance, model risk management, responsible AI principles, AI security controls, and enterprise AI operating models.
- Demonstrated ability to establish engineering standards, mentor senior engineers and engineering leaders, and build a culture of technical excellence.
- Experience delivering solutions across cloud-native ecosystems using Azure and modern DevOps, platform engineering, and software delivery practices.
- Experience driving architecture modernization and transformation initiatives within large enterprises.
- Experience in financial services, fintech, cybersecurity, or other heavily regulated industries.
- Demonstrated participation in industry forums, technology communities, open-source initiatives, vendor partnerships, or executive technology councils.
The annual base salary for this position is $240,000 - $360,000
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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Salary Context
This $240K-$360K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($300K) sits 42% above the category median. Disclosed range: $240K to $360K.
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/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 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).
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 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.
Frequently Asked Questions
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