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About This Role
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 l ocated 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 is a member of the AI \& Data Platform Enablement team responsible for defining the firm’s reusable AI patterns and managing the multi\-cloud platform on which AI solutions are built and deployed. This role owns the deployment and lifecycle management of AI models across the firm’s Microsoft Azure AI Foundry, AWS Bedrock, and Google Vertex AI environments and establishes standards for retrieval\-augmented generation (RAG), orchestration, APIs, and vector strategies. The AI Platform Engineer also manages the infrastructure that supports AI agents, including agent frameworks and associated vendor platforms. The AI Platform Engineer collaborates with Cloud Services, AI Development, Information Security, and third\-party vendors to ensure AI is built once and reused consistently across the firm.
Key Responsibilities
- Manages the firm’s AI control plane including deployment, versioning, and lifecycle of AI models across the firm’s multi\-cloud environments, including Microsoft Azure AI Foundry, AWS Bedrock, and Google Vertex AI
- Designs and manages the infrastructure supporting AI agents, including agent orchestration frameworks, runtime environments, and the controls around them
- Defines and maintains reusable AI architecture patterns including RAG, orchestration, prompt management, API design, and vector store strategies. Packages them as components solution teams can reuse
- Provides telemetry, logging, and audit data required for AI governance oversight
- Serves as a technical point of contact for AI platform vendors, partners, and internal teams in support of proofs of concept, integration, and operationalization
- Builds and maintains vector stores, embedding pipelines, and retrieval services used in AI solutions
- Establishes consistent CI/CD, environment, and infrastructure\-as\-code patterns for deploying and promoting AI workloads
- Collaborates with Information Security to ensure model deployments, agents, and platform services meet firm security, privacy, and compliance requirements
- Evaluates models, frameworks, and platform services across Azure, AWS, and GCP and recommends fit\-for\-purpose options balancing capability, cost, and risk
- Implements cost\-management and capacity practices for AI workloads across cloud providers
- Provides technical leadership, mentorship, and guidance to developers and solution teams
- Participates as a member of the AI Architectural Review Board to ensure AI solutions meet firm requirements
- Reviews existing AI implementations and recommends opportunities for better standardization, consolidation, or re\-architecture
- Authors and maintains platform documentation, reference architectures, and standard
- Creates and delivers technical presentations and training to technical and non\-technical audiences
- Appears on camera for meetings with colleagues and vendors
- Performs other duties as assigned by management
Qualifications
*Skills \& Competencies*
- Working knowledge of Azure AI Foundry/Azure OpenAI, AWS Bedrock, and/or Google Vertex AI model deployment and management
- Familiarity with AI agent frameworks and the infrastructure required to run and govern agents in production
- Proficiency with infrastructure\-as\-code (Terraform), containers (Docker, Kubernetes), and CI/CD pipelines
- Strong scripting and development skills in Python, PowerShell, and/or other languages, including REST API design and integration
- Solid understanding of cloud networking, identity, security, and cost\-management fundamentals
- Demonstrated ability to evaluate and manage third\-party AI vendors and platform
- Ability to communicate complex technical concepts clearly to technical and business audiences
- Strong attention to detail with solid time and project management skills
- Self\-motivated, able to work independently, and comfortable operating in a shared services model
*Education \& Prior Experience*
- Bachelor’s degree in computer science, information technology, or equivalent practical experience
- 7\+ years of experience in platform engineering, cloud solutions, or machine learning/AI engineering roles
- 3\+ years of hands\-on experience deploying or operating AI/ML workloads in a major cloud environment
- Demonstrated experience with multi\-cloud platforms
- (Azure, AWS, GCP) and AI model deployment
- Strong hands\-on experience deploying and managing AI/ML or large language models in at least one major cloud (Azure, AWS, or GCP); multi\-cloud experience strongly preferred
- Experience designing AI architecture patterns including RAG, orchestration frameworks (e.g., Semantic Kernel, LangChain), and vector databases
- Certifications in Azure, AWS, GCP, or AI/ML specialties preferred
- Experience working in a professional services organization strongly preferred. Law firm experience a plus
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Greenberg Traurig, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Greenberg Traurig AI Hiring
Greenberg Traurig has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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