Senior AI Platform Engineer (Data and Analytics Cloud Engineer)

Senior Engineer/Platform Leader

Accountable for designing, building, and operating secure, scalable AI/ML and Generative AI (GenAI) platforms in the cloud. This role develops and maintains reusable platform capabilities (e.g., model and prompt development environments, feature/model/prompt management, retrieval and knowledge-grounding patterns, data access patterns, CI/CD automation, evaluation/testing, and observability) so teams can deliver business outcomes faster while meeting Truist technology standards, security requirements, and regulatory obligations.

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.

• Design, build, and execute the AI/ML and GenAI platform strategy aligned to enterprise architecture, security, and risk standards.
• Own the engineering and lifecycle management of AI/ML platform components (e.g., development workspaces, training/inference patterns, model registry, feature storage patterns, experiment tracking, prompt/version management, retrieval-augmented generation (RAG) enablement, and reusable templates) for safe and deliberate consumption across the organization.
• Establish and champion DevSecOps practices for platform delivery, including GitLab source control, build automation, and CI/CD pipelines for infrastructure and application deployments.
• Deploy infrastructure as code (IaC) to the cloud using Terraform modules and pipelines; define standards for environments, networking, identity, secrets, encryption, logging, and configuration management.
• Partner with Cybersecurity, Risk, and other 2nd line of defense teams to implement and evidence required security controls (e.g., IAM least privilege, network segmentation, encryption, vulnerability management, audit logging, and policy-as-code) across platform services.
• Implement governance patterns for AI/ML and GenAI (e.g., model and prompt lifecycle controls, lineage/traceability for data, prompts, and outputs, approvals, change management, risk assessments, and operational readiness) consistent with enterprise data governance and regulatory obligations.
• Provide technical leadership and hands-on engineering to solve complex platform problems (performance, reliability, scalability, cost, and security), and guide engineers through designs, reviews, and delivery.
• Build platform reliability through automation and observability (monitoring, logging, tracing, SLOs), and partner with production support teams to increase resiliency, reduce toil, and improve time to recover.
• Enable self-service platform consumption via standardized APIs, reusable pipelines, templates, and documentation; in an Agile environment, may serve as an Agile/DevSecOps champion to accelerate delivery while maintaining compliance.

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. 1. Undergraduate degree in either computer science, analytics, data engineering, finance or equivalent degree. 2. At least 3 years of experience driving enterprise data strategy, data execution, data engineering or software delivery 3. Expert problem solving skills and being able to define detailed strategies 4. Experience in financial services or payments industry 5. Experience in meeting regulatory obligations and operating in a highly regulatory environment on the cloud 6. Experience in building a high performing team

Preferred Qualifications:

• Master's degree and/or 8+ years of progressive experience delivering complex cloud platforms, preferably supporting AI/ML or analytics workloads at enterprise scale.
• Experience building AI/ML platforms and/or MLOps capabilities (e.g., training/inference automation, model packaging and deployment, model registry, experiment tracking, and operational monitoring).
• Experience with container platforms and orchestration (e.g., Kubernetes/EKS), API enablement, and modern ML tooling (e.g., Python ML ecosystem) to operationalize models and GenAI services.
• Deep expertise in AWS (compute, networking, security/IAM, logging/monitoring, and managed services) and moderate experience with Azure services and deployment patterns.
• Hands-on DevOps/DevSecOps experience building CI/CD pipelines (GitLab), including automated testing, security scanning, artifact management, and controlled deployments across environments.
• Strong infrastructure-as-code experience deploying cloud components using Terraform; ability to build reusable modules and enforce standards/guardrails.
• Relevant cloud and security certifications (preferred), such as AWS Solutions Architect/DevOps Engineer, AWS Security Specialty, Azure Administrator/Architect, and/or Terraform certification; strong mentoring/coaching skills for engineers distributed across onshore and offshore teams.