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Description:
AI & Automation Workflow Specialist
Department: Applied AI / Innovation
Location: Harrisburg, Lancaster or Malvern, PA
Position Summary
Join one of the most innovative, forward-thinking law firms in the country. As a member of the Saxton & Stump team, you’ll be joining a culture of excellence and teamwork, mixed with a strong focus on investing in our team and the community. We understand that the new legal environment requires a different type of approach to all aspects of the business, and we’ve built that into the way we recruit, the way we structure our team, and the way we value customer service.
The AI & Automation Workflow Specialist will support the firm’s Applied AI function by helping build, test, troubleshoot, maintain, and improve AI-enabled workflows and automation solutions.
This is a hands-on technical role with meaningful attorney and end-user interaction. The right candidate will be able to take over much of the detailed technical execution work involved in building and maintaining workflows, while also being capable of speaking directly with attorneys, documenting workflows, supporting testing, and translating feedback into practical improvements.
This person does not need to be a traditional software engineer, but they must be technically fluent, detail-oriented, curious, and comfortable working with tools such as Microsoft Power Platform, Power Automate, Microsoft 365, and AI-enabled workflow platforms.
Key Responsibilities
Workflow Technical Execution
- Build, test, troubleshoot, and maintain workflows using Microsoft Power Automate, Power Platform, SharePoint, Teams, Forms, Excel, and related tools.
- Support the development of AI-enabled workflows, document automation, intake processes, approval flows, task routing, and internal productivity tools.
- Debug workflow errors, failed runs, logic issues, permissions problems, and integration issues.
- Refine workflows based on user feedback, testing results, and operational needs.
- Document workflow logic, requirements, dependencies, testing steps, known issues, and support procedures.
Requirements Gathering & User Interaction
- Participate in attorney-facing testing sessions, capture feedback, identify gaps, and recommend improvements.
- Explain workflow functionality and technical concepts in clear, practical terms for non-technical users.
Applied AI & Automation Support
- Help identify opportunities to use AI and automation to improve legal work, internal operations, client service, document processes, knowledge management, and administrative workflows.
- Assist with pilots, proofs of concept, testing, rollout, and post-launch support for AI and automation initiatives.
- Support both internal and client-facing AI and automation projects.
- Stay current on Microsoft Power Platform, generative AI, legal technology, and workflow automation capabilities.
Project Ownership & Support
- Take ownership of assigned workflow and automation tasks from intake through testing and implementation.
- Manage day-to-day execution details, including issue tracking, testing coordination, documentation, and user follow-up.
- Escalate strategic, security, governance, or architecture questions when appropriate.
- Help the Applied AI function scale by taking on the technical minutia required to move projects from concept to working solution.
Monitor production workflows and addresses any issues quickly.
Requirements: Required Qualifications
- Experience building or supporting workflows in Microsoft Power Automate or similar automation platforms.
- Strong knowledge of Microsoft 365 tools, including SharePoint, Teams, Forms, Excel, and Outlook.
- Strong troubleshooting, analytical, and problem-solving skills.
- Ability to communicate clearly with attorneys, executives, business users, and technical stakeholders.
- Strong attention to detail and comfort managing multiple workstreams.
- Willingness to learn new tools quickly and work hands-on in emerging AI and automation platforms.
Preferred Qualifications
- Experience in professional services.
- Experience with Power Apps, Dataverse, Copilot Studio, document automation, workflow mapping, or AI-enabled productivity tools.
- Experience creating process maps, user stories, test plans, training materials, or support documentation.
- Prior experience in a business analyst, automation specialist, legal technology, product owner, solutions analyst, or technical support role.
Physical Requirements
Administrative and support staff must be able to perform the essential functions of their roles, with or without reasonable accommodation, in a professional office setting. These requirements include:
- Prolonged periods of sitting or standing, depending on the nature of the role, and working on a computer or phone system.
- Frequent use of hands and fingers for typing, filing, handling paperwork, and operating standard office equipment (e.g., copiers, scanners, multi-line phones).
- Ability to communicate effectively, including speaking clearly and hearing adequately in person and via phone.
- Regular movement throughout the office, including walking, bending, reaching, and retrieving files or supplies.
- Ability to lift and carry office materials, files, or packages up to approximately 15-20 pounds on an occasional basis.
- Ability to maintain focus and organization while managing routine tasks, interruptions, and multiple priorities.
Ideal Candidate Profile
The ideal candidate is technically capable, intellectually curious, and execution-oriented. They enjoy building workflows, solving problems, testing details, and improving processes. They are comfortable getting into the details of automation logic and troubleshooting, but can also sit with an attorney, understand the business problem, and communicate clearly without unnecessary technical jargon.
This person should be eager to grow into a broader applied AI, legal technology, automation, or product ownership role. They should be comfortable operating in ambiguity, asking good questions, taking ownership, and helping turn loosely defined ideas into reliable, usable tools.
Success in This Role Looks Like
A successful AI & Automation Workflow Specialist will quickly become a trusted execution partner to the Director of Applied AI. They will help reduce the Director’s involvement in day-to-day workflow building, testing, debugging, and documentation, while providing reliable support to attorneys and staff as AI and automation tools are developed, tested, and deployed across the firm.
Saxton & Stump offers a competitive salary, as well as a comprehensive benefits package, including medical, dental, and vision insurance (which start day one of employment), a 401k plan, and the opportunity for professional growth.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Saxton & Stump, LLC, 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 in Demand for This Role
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 11,857 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Saxton & Stump, LLC AI Hiring
Saxton & Stump, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Harrisburg, PA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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