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About This Role
Our Corporate Resource Groups serve as the foundation for our success by providing vital support and specialized expertise to our various business units.
- Milwaukee, WI
- Information Technology
- R2026662
- Hybrid
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About the Role:
Baird is building out its AI capabilities, and the AI Acceleration team is at the center of that work. AI Acceleration is the IT team responsible for enabling AI technology across the firm, spanning everything from enterprise AI tools to custom agent solutions. This team is responsible for evaluating and deploying the tools, platforms, and solutions that make AI real for Baird associates and clients.
The AI \& Automation Tech Lead is a new role on this team, created to address two growing needs: a broader view across the AI technology landscape to guide the right solution approach for each problem, and deeper hands\-on capacity to enable and support the AI platforms and infrastructure that others across the firm use to build solutions.
This is a practitioner role, not an advisory one. You will be expected to assess business needs, recommend the right technology approach, and then roll up your sleeves and help deliver. The work is real, the stakeholders are engaged, and the expectation is outcomes. This hybrid position is based in our downtown Milwaukee office offering flexibility to work 2 days remote.
The Impact You’ll Make:
Broad AI Solutioning across the Full Tool Suite:
- Bring a broad perspective on AI tools and approaches, helping ensure Baird evaluates the right solution for each problem rather than defaulting to a single platform. Microsoft is the preferred ecosystem, but the ability to assess and recommend across vendors is important as the AI landscape continues to evolve
- Assess incoming AI needs and determine the right solution approach, including whether a problem calls for low\-code tooling, pro\-code development, or something in between
- Serve as a knowledgeable guide across the full range of AI tools and platforms in use at Baird. Baird is a Microsoft shop and Microsoft tools (Microsoft Azure AI services and M365\) are the default starting point, but the firm also has active use of Claude, ChatGPT, GitHub Copilot, and Rogo, and is expanding into additional platforms
- Partner with the AI Enablement Lead and business stakeholders to translate business needs into structured, actionable technology recommendations
- Identify repeatable solution patterns that can be documented and reused across the firm as AI capabilities scale
Hands\-on Platform Enablement and Technical Support:
- Take an active, hands\-on role in enabling the AI platforms and infrastructure the firm relies on, not just advising on them. This means standing up environments, configuring and supporting platforms, and being a go\-to technical resource for a given area of the stack. This is not a passive or purely advisory role
- Partner with IT delivery teams and business stakeholders to ensure AI platforms are properly configured, supported, and adopted across the firm
AI Landscape Awareness and Team Enablement:
- Actively monitor the AI technology landscape, tracking new tools, platforms, and approaches as they emerge and evaluating their relevance to Baird
- Serve as a resource for the broader AI Acceleration team, sharing knowledge, bringing new thinking to the group, and helping raise the level of AI expertise across the team
- Translate awareness of the evolving landscape into practical recommendations, helping the team and the firm stay ahead rather than react
Organizational Credibility and Communication:
- Build trust with both business and IT stakeholders as a knowledgeable, practical voice on AI technology, feasibility, and approach
- Communicate AI concepts clearly to non\-technical audiences, including business leaders, compliance, and risk stakeholders
- Navigate a matrixed corporate environment effectively, building alignment across teams and functions
What You’ll Bring to Baird:
- Proven, hands\-on experience doing this kind of work at another organization. Baird is looking to bring in established expertise, not develop it. Candidates should be able to point to specific experience assessing and recommending AI technology for given use cases, and enabling or supporting AI platforms and infrastructure in a prior role
- 5 or more years of experience in a technical AI, automation, or solution design role
- Broad and current knowledge of the AI technology landscape, including commercial platforms, agentic solutioning approaches, and LLM\-based tooling
- Hands\-on depth in at least one area of the AI tool stack, with direct experience enabling and supporting that platform at another organization, not just using it or overseeing others who managed it.
- Bring demonstrated, hands\-on depth in one or more of the following areas: Gen AI tools and productivity platforms (e.g., M365 Copilot, Claude, ChatGPT, GitHub Copilot), Business Agent platforms (e.g., Copilot Studio, Power Automate, Cowork) or Azure AI services platforms (e.g., Azure AI foundry, Azure AI Search). Familiarity with the Microsoft ecosystem is preferred, and broader knowledge across the AI landscape is a plus
- Demonstrated ability to evaluate technology options across vendors and approaches and recommend the right fit for the problem, rather than defaulting to a preferred platform
- Working fluency across AI tool categories, including enterprise AI productivity tools, low\-code business agent platforms, and familiarity with pro\-code or custom agent approaches
- Comfort working with ambiguity, moving between strategic framing and ground\-level execution as the work demands
- Strong communication and relationship\-building skills, with a track record of earning credibility across both technical and business audiences
- Experience working within or alongside risk, compliance, and governance functions in a regulated industry is a plus
- Equivalent practical experience in lieu of a formal degree is fully accepted. What matters is what you’ve built and delivered, not where you studied
Why This Role:
Baird has built real AI momentum, and the AI Acceleration team is where that momentum is being turned into tangible outcomes for the firm. This role sits at the intersection of two things that are hard to find in one person: the breadth to navigate a complex, fast\-moving technology landscape and the hands\-on ability to actually deliver.
You will have a meaningful voice in how AI solutions are shaped and deployed at Baird, working alongside a team that brings strong technical foundations and genuine curiosity about what AI can do. This is not a role defined by slide decks and recommendations. It is defined by what gets built and what gets used.
Baird is an employee\-owned firm with a strong culture of collaboration and long\-term thinking. The expectation here is to do good work, build real trust, and contribute to something that will matter to the firm for years to come.
\#LI\-KC1
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More About Our Firm
-----------------------
### The Baird Way
What we call The Baird Way is the foundation of core beliefs we share, the promise we make to each other and the inspiration for our mission.
### Our Multicultural Community Conference
This conference unites colleagues from all corners of our firm to honor and celebrate the contributions of our ethnically diverse associates.
### Honored to be a Best Place to Work
More than 20 consecutive years of recognition as a great workplace makes Baird a destination of choice for extraordinary people from across our industry.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Baird, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Baird AI Hiring
Baird has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Milwaukee, WI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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