AI Platform Engineer

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 management.

- Strong production operations skills, including observability, alerting, incident response, root-cause analysis, change management, capacity planning, and recovery.

- Ability to build and maintain reusable infrastructure-as-code using tools such as Terraform or Bicep and adopt the firm’s approved tooling and engineering standards.

- Proficiency in Python and/or PowerShell for API-driven provisioning, configuration, operational automation, and troubleshooting.

- Experience with Git, code review, and CI/CD pipelines, including testing, deployment approvals, secure configuration, and rollback.

- Practical knowledge of Entra ID, managed identities, service principals, secrets management, private connectivity, DNS, and least-privilege access.

- Understanding of APIs, search, indexing, and retrieval patterns sufficient to enable platform integration and troubleshoot with data and application engineers.

- Understanding of governed data access, source permissions, confidentiality, and safeguards for sensitive information in platform telemetry.

- Ability to analyze usage attribution, cost, performance, quotas, and capacity and implement practical optimization and cost-control measures.

- Strong problem-solving, documentation, communication, and delivery skills; ability to collaborate with vendors and technical and non-technical stakeholders.

Education & Prior Experience

- Bachelor’s degree in computer science, information technology, or equivalent practical experience.

- 5+ years of experience in platform engineering, cloud engineering, site reliability engineering, or AI infrastructure roles.

- Hands-on experience building and operating Microsoft Azure services in an enterprise production environment, including deployment, troubleshooting, and lifecycle management.

- Production experience operating at least one managed AI or large language model platform, such as Azure AI Foundry, Azure OpenAI, or an equivalent service.

- Demonstrated delivery of infrastructure-as-code, Python and/or PowerShell automation, and Git-based controlled deployment pipelines.

- Experience with AWS Bedrock, Google Vertex AI, or comparable platforms preferred. Docker/Kubernetes experience and relevant cloud or AI certifications are a plus.

- Experience working in a professional services organization strongly preferred. Law firm experience a plus.

Physical Requirements:

- While performing the duties of this job, the employee is occasionally required to move from workstation or desk, throughout the work area to work independently or with a team, to meet with colleagues or supervisor and retrieve work assignments.

- This position may also be sedentary and require the employee to sit for extended periods of time.

- Requires manual dexterity to dial a telephone, enter data into a computer, handle objects and operate tools.

GT is an EEO employer with an inclusive workplace committed to merit-based consideration and review without regard to an individual’s race, sex, or other protected characteristics and to the principles of non-discrimination on any protected basis.