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About This Role
Vedder's Chicago office is seeking an AI Solutions Engineer. The AI Solutions Engineer is responsible for designing, building, and deploying AI\-powered agents that automate and enhance legal and business workflows across the firm. This role is hands\-on and development\-focused, with a primary emphasis on creating production\-ready AI agents using Microsoft Copilot Studio and related technologies. The position leverages applied AI, intelligent automation, and enterprise integrations to deliver scalable, user\-facing solutions. Acting as a bridge between business and technology, the role translates legal and operational needs into functional AI agents and automation solutions that drive efficiency, improve knowledge access, and support adoption of emerging AI capabilities.
As an AI Solutions Engineer, your duties will include but not be limited to:
- Design, develop, and deploy AI\-powered agents utilizing Microsoft Copilot Studio and related technologies.
- Architect and implement end\-to\-end agent workflows, including input processing, document ingestion, and response generation.
- Configure, test, and optimize prompts, logic, and orchestration to ensure accuracy, reliability, and consistent performance.
- Collaborate with attorneys and business stakeholders to identify high\-value use cases and translate requirements into scalable, production\-ready solutions.
- Work with IT department to integrate AI solutions with enterprise platforms (e.g., SharePoint, document management systems, Intapp, Outlook, and Microsoft Word) through APIs and connectors.
- Evaluate and enhance business processes by implementing AI\-driven automation and standardized solution patterns.
- Monitor solution performance, usage, and adoption, and drive continuous improvement of deployed agents.
- Ensure all solutions adhere to firm security, confidentiality, and AI governance standards, including responsible AI practices.
- Develop and maintain comprehensive documentation of solution architecture, workflows, and agent configurations to support scalability and long\-term maintainability.
Skills \& Competencies:
- Demonstrated ability to design and deliver AI\-driven solutions and agent\-based automation within complex business environments.
- Strong understanding of AI solution architecture, including agent design, workflow orchestration, and system integration.
- Proficiency in translating business requirements into scalable, production\-ready technology solutions.
- Ability to collaborate effectively with cross\-functional teams and communicate complex technical concepts clearly to non\-technical stakeholders, including attorneys and business leaders.
- Strong analytical and problem\-solving skills, with a focus on process optimization and automation.
- Sound understanding of enterprise governance, data security, and responsible AI practices.
- Highly organized with strong attention to detail and commitment to quality, accuracy, and reliability.
- Adaptability and continuous learning mindset to stay current with emerging AI technologies and industry trends.
Qualifications \& Required Experience:
- 2\+ years of experience in AI solutions, automation, business systems, or related roles within a professional services or enterprise environment.
- Proficiency with agent development platforms (e.g., Microsoft Copilot Studio or similar tools), including conversational design, workflow orchestration, and prompt configuration.
- Experience developing and integrating end\-to\-end automation workflows that embed AI into business processes.
- Strong understanding of AI concepts, including large language models (LLMs), prompt design, and agent\-based architectures.
- Familiarity with retrieval\-augmented generation (RAG), grounded knowledge architectures, and enterprise content integration.
- Strong understanding of business processes and operational efficiency, with the ability to identify and implement improvement opportunities.
- Understanding of data structures, system integration patterns, and information flow across enterprise applications.
- Demonstrated ability to manage competing priorities and deliver high\-quality work within defined timelines.
- Bachelor’s degree in Information Systems, Computer Science or a related field.
- Prior experience in a law firm or professional services environment preferred.
Computer Skills:
*To perform this job successfully, an individual must be proficient in the following software:*
- AI platforms: Microsoft Copilot Studio as well as other generative AI platforms.
- AI frameworks: familiarity with agent frameworks, prompt orchestration, and retrieval\-based architectures.
- Automation tools: Microsoft Power Platform (Power Automate, Power Apps).
- Data platforms: document management systems, databases, and knowledge repositories.
- Integration technologies: APIs, connectors, and enterprise system integrations.
- Analytics \& monitoring tools to track performance, usage, and adoption.
Compensation Range: $75,000/yr. to $100,000/yr
At Vedder, we believe in recognizing and rewarding our employees' contributions. Our comprehensive Total Rewards Package includes:
- Competitive Salary: We offer a competitive base salary commensurate with skills and experience.
- Bonus Program: Discretionary annual bonus program.
- Retirement Planning: Discretionary profit sharing and 401(k) matching to help you plan for your future.
- Health and Wellness: Comprehensive health, dental, and vision plans, along with optional health savings and flexible spending accounts, firm\-paid Life and Disability benefits, and wellness programs to support your overall well\-being.
- Paid Time Off: Competitive time off package including vacation days, paid holidays, sick time and personal days.
- Professional Development: Opportunities for continuous learning and career growth through firm provided training programs.
- Employee Recognition: Anniversary and Vedder Praise Programs to celebrate your achievements and milestones.
- Work\-Life Balance: Hybrid work model and family\-friendly policies.
- Additional Perks: Employee discount program, pre\-tax commuter benefits, back up child \& elder care, Employee Assistance Program (EAP), fitness center discounts and more.
Join Vedder and be part of a team that values hard work and dedication!
Equal Employment Opportunity
Vedder Price P.C. is an equal opportunity employer. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability (where applicant is qualified to perform the essential functions of the job with or without reasonable accommodations), medical condition, protected veteran status, gender identity, genetic information, or any other characteristic protected by federal, state, or local law. We participate in E\-verify.
Applicants who are interested in applying for a position and require special assistance or an accommodation during the process due to a disability should contact the Vedder Recruiting Team at [email protected]
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $75K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 Vedder Price, 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. This role's midpoint ($87K) sits 60% below the category median. Disclosed range: $75K to $100K.
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.
Vedder Price AI Hiring
Vedder Price has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $100K - $100K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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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