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About This Role
About Parker Poe
Parker Poe is a business law firm that advises clients on the decisions that define them and the moments in between. Rooted in the Southeast, the firm is committed to providing exceptional client experiences and practical solutions, with the national and international reach to serve clients wherever their business takes them. Our firm offers a career experience built around deep relationships, sophisticated work, collaboration, mentoring, professional development, community, and growth.
Position Summary
Parker Poe is seeking an AI Workflow & Skills Engineer to help transform how our attorneys and business professionals work by building custom AI agents, automations, and productivity tools. In this innovative role, you’ll take workflow requirements and turn them into practical, user-friendly solutions. This is a high-visibility opportunity to build AI solutions that move from concept to production.
Working closely with our AI Enablement Manager and cross-functional teams, you’ll design, test, deploy, and continuously improve AI-powered tools that streamline legal work, enhance productivity, and deliver meaningful business value. This role is ideal for someone who is a creative problem-solver who enjoys building with emerging technologies and wants to play a key role in shaping the future of AI at Parker Poe.
This full-time, exempt position may be based in any Parker Poe office, with a preference for Charlotte, North Carolina and will require periodic travel to support firmwide initiatives.
Essential Duties and Responsibilities
AI Solution Development
- Translate attorney workflows and business needs into practical AI-powered tools, automations, and solutions.
- Build, test, and iterate on custom AI skills and agents using tools such as Claude Enterprise, ChatGPT Enterprise, Microsoft Copilot and related agentic frameworks.
- Integrate AI tools with the firm's existing technology ecosystem, creating seamless workflows across document management, practice management, email/calendaring, e-signature, SharePoint, and other key business platforms.
- Develop reusable prompt libraries, templates, and scalable components that accelerate AI adoption and enable solutions to be deployed across multiple practice groups.
Quality, Governance and Documentation
- Ensure AI solutions are production-ready through rigorous testing, validation, and quality reviews focused on accuracy, reliability, responsible AI use, and minimizing hallucination risk.
- Partner with IT, Information Security, and Risk/Compliance to ensure all tools safeguard client information and comply with firm policies and regulatory requirements.
- Create clear documentation and implementation guidance that supports user adoption, ongoing maintenance, and limitations for internal reference.
Adoption, Support and Continuous Improvement
- Assist attorneys and business professionals in embracing AI technologies through training, best-practice recommendations, and ongoing support.
- Manage and prioritize a portfolio of AI initiatives, focusing on solutions that deliver the greatest value to attorneys and align with firm strategic goals.
- Monitor the performance and adoption of AI solutions, leveraging user feedback and data-driven insights to drive continuous improvement and innovation.
- Perform other duties as assigned.
Qualifications
- Associate’s degree in computer science, information systems, data analytics, or a related field, or equivalent combination of education and IT experience.
- Experience in a law firm, professional services, or other regulated environment is preferred, particularly where confidentiality and data governance are critical.
- Experience building and optimizing AI-powered solutions using Claude, ChatGPT, Copilot or similar large language model (LLM-based) platforms, including prompt engineering, custom instructions, and agent/tool configuration.
- Advanced proficiency in Microsoft Office applications, including Word, Excel, PowerPoint, Outlook, and Teams.
- Familiarity with legal workflows and legal technology preferred.
- Experience working with application programming interfaces (APIs), automation tools, and scripting languages such as Python, JavaScript, or similar technologies to connect AI solutions with business systems and workflows.
- Demonstrated ability to manage multiple concurrent AI development efforts, communicate clear timelines, and improve solutions through iterative testing and user feedback.
- Strong ability to translate business requirements into practical solutions, proactively seeking clarification, identifying gaps, and driving execution.
- Strong written, verbal, interpersonal, and customer-service skills, with the ability to provide responsive, solutions-oriented support.
- Excellent attention to detail and a quality-first mindset.
Traits for Success
This role is ideal for someone who loves building with emerging technologies, solving complex challenges, and creating real-world solutions. They are equally comfortable navigating ambiguity, collaborating with stakeholders, and driving projects forward. We are looking for someone who is:
- A creative problem-solver who enjoys transforming complex business challenges into user-friendly AI solutions.
- A quality-focused builder who takes pride in creating accurate, reliable, and well-designed solutions that users trust.
- A proactive self-starter who can navigate evolving requirements, ask thoughtful questions, and move projects forward with confidence.
- A collaborative partner who works effectively with attorneys, cross-functional teams, and stakeholders to deliver meaningful results.
- A lifelong learner who is passionate about AI, emerging technologies, and continuously improving the way people work.
Parker Poe is an Equal Opportunity Employer and makes recruitment, employment, promotional and all other personnel decisions without regard to race, color, religion, national origin, age, sex, sexual orientation, gender identity, marital status, disability, veteran status or any other characteristic protected by law.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Parker Poe Adams & Bernstein LLP, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Parker Poe Adams & Bernstein LLP AI Hiring
Parker Poe Adams & Bernstein LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charleston, SC, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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