Senior AI Platform Engineer (Data and Analytics Cloud Engineer)

Truist Bank•Atlanta, VA

11d•$130,000 - $170,000•Onsite

About The Position

The position is described below. If you want to apply, click the Apply Now button at the top or bottom of this page. After you click Apply Now and complete your application, you'll be invited to create a profile, which will let you see your application status and any communications. If you already have a profile with us, you can log in to check status. Need Help? If you have a disability and need assistance with the application, you can request a reasonable accommodation. Send an email to Accessibility (accommodation requests only; other inquiries won't receive a response). Regular or Temporary: Regular Language Fluency: English (Required) Work Shift: 1st shift (United States of America) Please review the following job description: 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.

Requirements

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

Nice To Haves

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.

Responsibilities

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.