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Regular or Temporary:

Regular

Language Fluency:  English (Required)

Work Shift:

1st shift (United States of America)

Please review the following job description:

The Senior Director of Agentic Engineering leads the engineering, evolution, and operation of enterprise foundation services that enable teams to securely build, deploy, govern, and operate agentic AI solutions at scale. This leader establishes engineering direction and drives the development of reusable agentic capabilities, enterprise context services, patterns, controls, integrations, and automation that accelerate adoption while meeting enterprise expectations for security, resiliency, portability, observability, traceability, and operational readiness.

The role requires a combination of deep, first-hand agentic AI and AWS engineering expertise and senior technology leadership. The successful candidate must operate effectively at both the engineering and organizational levels, with sufficient technical depth to guide architecture and engineering decisions while building teams, establishing standards, managing complex dependencies, and evolving capabilities into durable enterprise foundation services.

This leader will assume responsibility for an existing body of agentic engineering and take that foundation forward through its full engineering lifecycle, continuously evolving, enhancing, extending, scaling, and operating these capabilities as enterprise services. The role will also define the engineering model through which agents increasingly operate as accountable contributors within the enterprise workforce, with clear ownership, autonomy, governance, and lifecycle management.

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.

1. Lead Agentic Engineering as an enterprise engineering capability, providing technical and organizational leadership for teams responsible for agentic AI foundation services, frameworks, reusable components, engineering patterns, and platform integrations.

2. Own the engineering lifecycle of enterprise agentic foundation services, from architecture, design, and implementation through deployment, adoption, operation, modernization, optimization, and ongoing evolution.

3. Lead the engineering and evolution of AWS-based agentic capabilities, with particular emphasis on Amazon Bedrock and Amazon Bedrock AgentCore, including governed access to models, agents, tools, knowledge, data, and enterprise systems.

4. Define the operating model for agents as accountable contributors within the enterprise workforce, including ownership and accountability, autonomy tiers, permitted actions, oversight, controls, and lifecycle management from provisioning and deployment through modification and retirement.

5. Establish reusable engineering patterns that enable product and application teams to build agentic solutions safely and consistently, reducing one-off implementation and creating repeatable approaches for onboarding, deployment, integration, operation, and lifecycle management.

6. Drive the evolution from human-executed processes toward self-service, automated, and agent-executed capabilities, establishing the engineering patterns, controls, and operational mechanisms needed for agents to perform increasingly sophisticated activities safely and autonomously.

7. Own the enterprise context layer as a product, providing governed capabilities through which agents discover, access, interpret, and use enterprise knowledge, data, relationships, semantics, and context. Establish reusable approaches for knowledge bases, retrieval, graph and ontology capabilities, memory, data integration, and contextual grounding.

8. Drive the engineering of agentic control-plane capabilities, including gateways, authorization and policy enforcement, identity and access controls, model access, guardrails, agent and tool registration, deployment automation, private connectivity, and operational controls.

9. Establish provider-portable agentic engineering patterns that minimize unnecessary coupling to individual models and model providers and allow agentic capabilities to evolve as the model ecosystem advances. Establish evaluation-gated promotion practices so model and provider changes are validated against defined quality, risk, performance, cost, and operational criteria before production adoption.

10. Maintain a forward-leaning agentic engineering roadmap, proactively evaluating and developing enterprise patterns for emerging capabilities including long-running agents, multi-agent systems, advanced orchestration, and computer-use patterns so foundational engineering capabilities can develop ahead of business demand.

11. Engineer security, risk, privacy, and governance into the platform, working closely with enterprise architecture, cybersecurity, risk, data, infrastructure, and other control partners so appropriate controls are integral to the engineering lifecycle rather than added after implementation.

12. Advance observability, traceability, and operational engineering for agentic systems, ensuring teams can understand agent and system behavior and support production workloads using appropriate logging, monitoring, telemetry, auditability, and operational controls.

13. Build automation and infrastructure-as-code patterns for repeatable deployment and onboarding, progressing toward scalable self-service and agent-executed provisioning, configuration, testing, deployment, and operational activities where appropriate.

14. Establish engineering and evaluation standards for production-quality agentic systems, including software engineering practices, agent and model evaluation, release management, resiliency, performance, security, operational readiness, and lifecycle management.

15. Lead complex engineering initiatives across organizational boundaries, actively managing technical dependencies, sequencing, critical-path work, engineering decisions, and delivery risks.

16. Partner with product, strategy, architecture, security, data, infrastructure, application, and business teams to translate emerging agentic AI needs into prioritized engineering capabilities and reusable enterprise services.

17. Provide authoritative technical leadership on agentic engineering, remaining sufficiently close to the technology to review designs, challenge engineering assumptions, evaluate emerging capabilities, resolve difficult technical problems, and make informed build, integrate, or adopt decisions.

18. Develop and execute short- to mid-term engineering and operational plans aligned with broader AI, data, cloud, and enterprise technology strategies.

19. Build and develop a high-performing engineering organization, including workforce planning, talent acquisition, technical capability development, coaching, performance management, succession planning, and development of future engineering leaders.

20. Manage third-party and strategic technology relationships as required, ensuring external capabilities and engineering contributions conform to enterprise architecture, engineering, security, operational, and quality expectations.

21. Steward the financial and operational resources of the Agentic Engineering organization, including workforce, technology consumption, vendor expenditures, and other associated costs.

22. Drive continuous modernization and simplification, using experience from production workloads and new use cases to improve the underlying foundation, remove friction, increase reuse, strengthen controls, improve portability, and increase engineering and operational efficiency.

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.
Essential Duties and Responsibilities

• Bachelor’s degree or higher in Computer Science, Engineering, Information Technology, or a related discipline, or equivalent education, training, and work-related experience.
• Minimum of 12 years of progressive technology and engineering experience, including significant experience leading complex engineering organizations and enterprise technology initiatives.
• Demonstrated experience building, leading, and developing high-performing engineering teams responsible for complex, production-scale technology platforms or foundation services.
• Deep, first-hand engineering experience with generative AI and agentic AI systems, with the technical credibility to engage directly in architecture, design, implementation, troubleshooting, and engineering trade-off discussions.
• First-hand implementation experience with Amazon Bedrock and Amazon Bedrock AgentCore, including experience building or integrating production-oriented agentic capabilities on AWS.
• First-hand experience with the following AWS technologies and their application within secure enterprise architectures:
  • Amazon Bedrock
  • Amazon Bedrock AgentCore
  • Amazon Bedrock Guardrails
  • Amazon Bedrock Knowledge Bases
  • Amazon Bedrock Data Automation
  • Amazon Neptune for graph and ontology use cases
  • AWS Glue for data integration and catalog-related capabilities
  • Amazon CloudWatch and AWS CloudTrail for monitoring, logging, and traceability
  • AWS Identity and Access Management (IAM)
• Demonstrated understanding of agentic architecture and engineering patterns, including agent orchestration, tool integration, grounding and knowledge retrieval, context and memory, authorization, human oversight, and the controls required to safely operate agentic systems.
• Demonstrated experience designing agentic systems with explicit approaches to agent identity, ownership, authorization, autonomy, human oversight, auditability, and lifecycle management.
• Experience designing AI platforms and applications for model and provider portability, including systematic evaluation of model quality, safety, performance, cost, and fitness for purpose and the use of evaluation criteria to govern promotion into production environments.
• Demonstrated experience engineering enterprise context and knowledge capabilities, including retrieval-augmented generation, knowledge bases, graph and ontology-based approaches, enterprise data integration, semantic context, and agent memory.
• Strong software and platform engineering experience establishing reusable services, APIs, infrastructure-as-code, CI/CD practices, engineering standards, observability, and operational controls.
• Demonstrated experience designing and operating secure cloud architectures, including identity and access management, workload isolation, network controls, policy enforcement, logging, monitoring, and auditability.
• Proven ability to solve difficult, multi-team engineering problems involving dependencies across applications, cloud infrastructure, security, data, architecture, and enterprise governance.
• Strong executive and technical communication skills, with the ability to move effectively between detailed engineering discussions and senior-level business, risk, and technology conversations.
• Demonstrated ability to lead technology transformation in a complex enterprise environment while balancing innovation, delivery velocity, resiliency, security, risk, and governance.

Preferred Qualifications

• Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
• Experience establishing or leading enterprise AI, generative AI, agentic AI, cloud platform, or developer platform engineering organizations.
• Experience building shared foundation services consumed by multiple application or product teams, particularly where standardization, reuse, developer experience, and governed self-service are key objectives.
• Experience engineering AI capabilities within a regulated financial-services or similarly regulated environment.
• Experience with graph-based knowledge representation, ontologies, retrieval-augmented generation, enterprise knowledge integration, semantic architectures, and advanced grounding patterns.
• Experience establishing technical approaches to AI and agent evaluation, responsible AI controls, traceability, human oversight, and production monitoring.
• Experience evaluating and engineering emerging agentic patterns, including long-running agents, multi-agent orchestration, and computer-use capabilities.
• AWS technical certifications or comparable demonstrated expertise in AWS architecture and engineering.
  

The annual base salary for this position is $260,000 - $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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