AI Platform Engineer at Greenberg Traurig in Atlanta, GA

Job description

Greenberg Traurig (GT), a global law firm with locations across the world in 15 countries, has an exciting employment opportunity for you. We offer competitive compensation and an excellent benefits package, along with the opportunity to work within an innovative and collaborative environment. Join our Technology Team as an AI Platform Engineer located in various offices. We are seeking a professional who thrives in a fast-paced, deadline-driven environment. The ideal candidate possesses strong problem-solving and decision-making abilities, ensuring efficiency and accuracy in every task. With a dedicated work ethic and a can-do attitude, you will take initiative and approach challenges with confidence and resilience. Excellent communication skills are essential for collaborating effectively across teams and delivering exceptional client service. If you are someone who demonstrates initiative, adaptability, and innovation, we invite you to join our team. This role can be based in various offices, on a hybrid basis. This role reports to the Director of Enterprise Content and Cloud Services. Position Summary: The AI Platform Engineer builds, automates, and operates shared cloud AI services for the firm’s AI development and solution teams. Working primarily in Microsoft Azure, the engineer owns AI platform configuration, hosted model deployments and endpoints, access controls, observability, capacity, costs, and production readiness, with additional responsibility for approved AI services in AWS, Google Cloud, and vendor environments. The role provides secure, reusable capabilities for AI applications and agents and partners with Data Engineering on governed access to enterprise information. This is a hands-on platform engineering and operations role, not a model-training or application feature-development role. Key Responsibilities: Builds and operates shared AI platform services, primarily in Azure, including model deployments, endpoints, configuration, upgrades, and retirement of model versions and services. Develops reusable automation for provisioning, model deployment, access configuration, and monitoring using version-controlled infrastructure-as-code, scripts, and controlled deployment pipelines. Monitors availability, latency, errors, and utilization; troubleshoots identity, networking, quota, and service issues; and leads platform incident response and vendor escalation. Maintains platform inventories, telemetry, audit records, and operational reporting, protecting client and firm information in logs under approved data-handling and retention requirements. Partners with security, cloud, identity, privacy, governance, and engineering teams to implement approved identity, least-privilege access, network-access, secrets-management, and logging controls. Builds secure, reusable platform access patterns for approved enterprise data and knowledge sources, preserving source permissions and implementing approved confidentiality controls. Establishes usage attribution, quota controls, budget monitoring, and capacity reporting; optimizes consumption, deployment choices, and cost without compromising service requirements. Defines service health indicators, alerting, runbooks, rollback procedures, and production-readiness criteria; tests changes before release and maintains lifecycle and recovery plans. Authors technical standards, reference architectures, and operational documentation; participates in architecture reviews and delivers training and technical guidance to solution teams. Serves as a technical advisor to vendors, stakeholders, and solution teams and provides guidance on platform design and operations. Appears on camera for meetings with colleagues and vendors. Performs other duties as assigned by management. Qualifications: Skills & Competencies Strong Azure platform engineering skills and understanding of managed AI services, including model deployments, endpoint authentication, quotas, versioning, and lifecycle manag