AI Engineer (Hybrid)

About the Company

Greenberg Traurig, LLP is a global law firm with offices across 15 countries, providing legal services to clients in a wide range of industries. The firm is recognized for its innovative approach, collaborative culture, and commitment to delivering exceptional client service. Greenberg Traurig leverages advanced technologies to enhance business operations while maintaining the highest standards of security, governance, and compliance.

About the Role

The AI Platform Engineer is responsible for designing, deploying, and managing the firm's enterprise AI platform across multi-cloud environments. This role oversees AI model lifecycle management, reusable AI architecture patterns, AI agent infrastructure, and cloud-based AI services while collaborating with development, cloud, security, and business teams to build scalable, secure, and reusable AI solutions.

Location: Atlanta, Georgia (Hybrid)

Responsibilities

• AI Platform Engineering
  • Manage the enterprise AI platform across multi-cloud environments.
  • Deploy, version, and maintain AI and machine learning models throughout their lifecycle.
  • Ensure AI services are scalable, reliable, and reusable across the organization.
  • Support enterprise AI platform operations and continuous improvements.
  • Maintain high availability and performance of AI infrastructure.
• AI Architecture & Solution Design
  • Design reusable AI architecture patterns for enterprise applications.
  • Develop standards for Retrieval-Augmented Generation (RAG), prompt management, orchestration, APIs, and vector strategies.
  • Build reusable platform components for development teams.
  • Define best practices for enterprise AI implementation.
  • Support standardization of AI architecture across business solutions.
• AI Infrastructure & Cloud Management
  • Manage AI workloads across Microsoft Azure, AWS, and Google Cloud environments.
  • Design and maintain infrastructure supporting AI agents and runtime environments.
  • Implement Infrastructure as Code (IaC) using Terraform.
  • Manage containerized deployments using Docker and Kubernetes.
  • Support cloud networking, identity management, and platform security.
• Model Deployment & Lifecycle Management
  • Deploy, monitor, update, and retire AI models across enterprise environments.
  • Manage model versioning and release processes.
  • Evaluate AI models for performance, scalability, and operational readiness.
  • Support continuous integration and continuous deployment (CI/CD) pipelines.
  • Optimize AI workloads for reliability and operational efficiency.
• Data & AI Services
  • Build and maintain vector databases, embedding pipelines, and retrieval services.
  • Support AI orchestration frameworks and agent platforms.
  • Develop and integrate REST APIs supporting AI applications.
  • Implement telemetry, logging, and audit capabilities for AI governance.
  • Monitor platform performance and data quality.
• Security, Governance & Compliance
  • Collaborate with Information Security teams to ensure secure AI deployments.
  • Implement governance controls for AI platforms and services.
  • Ensure compliance with organizational security, privacy, and regulatory requirements.
  • Support AI risk management and governance initiatives.
  • Promote secure and responsible AI development practices.
• Platform Evaluation & Optimization
  • Evaluate AI models, cloud services, and third-party platforms.
  • Recommend technology solutions based on capability, cost, scalability, and risk.
  • Implement cloud cost optimization and capacity planning strategies.
  • Identify opportunities for platform standardization and modernization.
  • Recommend architectural improvements to existing AI implementations.
• Collaboration & Technical Leadership
  • Partner with Cloud Services, AI Development, Information Security, and business stakeholders.
  • Serve as a technical advisor for AI platform initiatives and vendor integrations.
  • Mentor developers and engineering teams on AI platform best practices.
  • Participate in architecture review boards and technical planning sessions.
  • Deliver technical presentations, documentation, and knowledge-sharing sessions.

Qualifications

Required

• Bachelor's degree in Computer Science, Information Technology, or equivalent practical experience.
• Seven or more years of experience in platform engineering, cloud engineering, or AI/ML engineering.
• Three or more years of hands-on experience deploying AI or machine learning workloads in a major cloud platform.
• Experience with Microsoft Azure, AWS, or Google Cloud Platform, with multi-cloud experience preferred.
• Strong knowledge of AI model deployment and lifecycle management.
• Experience with Infrastructure as Code using Terraform.
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Proficiency in Python, PowerShell, and REST API development.
• Strong understanding of cloud networking, identity, security, and cost management.
• Excellent analytical, problem-solving, communication, and project management skills.

Preferred

• Experience designing Retrieval-Augmented Generation (RAG) architectures.
• Experience with orchestration frameworks such as LangChain or Semantic Kernel.
• Experience with vector databases and AI agent frameworks.
• Cloud or AI certifications in Azure, AWS, or Google Cloud.
• Experience working in a professional services organization.
• Previous law firm experience.

Pay & Benefits

• Competitive compensation.
• Comprehensive employee benefits package.
• Hybrid work environment.
• Professional development and learning opportunities.