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About This Role
Forge ahead with WSB. The Sr Manager, AI & Data is responsible for leading the organization's enterprise artificial intelligence, data, and application portfolio strategy to drive innovation, operational efficiency, and business value across the firm. This role partners closely with executive and business leaders to identify, prioritize, and implement AI-enabled solutions, establish enterprise data strategy, and oversee application development and product management functions. The Senior Manager, AI & Data provides strategic leadership, governance, and technical direction to ensure AI, data, and technology investments align with business objectives and are successfully adopted across the organization. WSB was founded on the understanding that culture drives results, and we've dedicated ourselves to the value of strong relationships, collaboration, and forward-thinking ideas. We've inspired each other to discover thoughtful and creative solutions that look beyond the needs of today to the opportunities of tomorrow. We invite you to forge ahead with us.
What you will do:
- Develop, execute, and maintain the organization's AI strategy and roadmap, identifying priorities, defining success measures, and ensuring alignment with business objectives.
- Partner with leaders across the organization to identify, prioritize, and implement high-value AI, automation, and data-driven solutions that improve operational effectiveness and business performance.
- Lead the evaluation, design, implementation, and lifecycle management of AI technologies, applications, and enterprise platforms, including build, buy, and retirement decisions.
- Drive enterprise-wide adoption of AI capabilities through change management, training programs, governance, communication, and performance measurement.
- Provide strategic and technical leadership for software development teams utilizing AI-augmented delivery practices to improve team capacity, efficiency, and delivery outcomes.
- Own the enterprise application portfolio, including product strategy, roadmap development, prioritization, support models, performance management, and lifecycle governance.
- Lead the development and execution of the enterprise data strategy in partnership with data engineering teams, ensuring alignment of data capabilities, investments, and business priorities.
- Establish and maintain technology standards related to architecture, application design, APIs, integrations, security, documentation, code quality, and operational support.
- Collaborate with technology, security, and business leaders to establish and operationalize AI governance, acceptable use standards, risk management practices, and citizen development frameworks.
- Provide architectural guidance and oversight for enterprise technology solutions, ensuring scalability, security, reliability, and alignment with organizational goals.
- Monitor industry trends, emerging technologies, and evolving AI capabilities to identify opportunities that create sustainable business value.
- Build strong partnerships with executive leadership and key stakeholders, translating business needs into technology strategies and actionable solutions.
- Track, analyze, and communicate key performance indicators, including AI adoption, business outcomes, technology delivery metrics, and organizational efficiency gains.
- May remain in stationary position for extended periods.
- Perform other duties as assigned.
People Leadership:
- Demonstrates expanded leadership across larger teams, broader scope, and/or increased operational complexity compared to the Manager level.
- Executes specific goals & operational plans for assigned teams against Group plans.
- Plans for upcoming work, allocates budgets, & schedules needed resources.
- Maintains adherence to quality standards and administrative policies (e.g., time sheets, expense reports).
- Leads people through coaching and development, effective performance management, and hiring and workforce planning.
What you will bring:
- Education:
- Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related field; or an equivalent combination of education and experience.
- Master's degree in a related discipline is preferred.
- Experience:
- Progressive leadership experience in technology, software engineering, data, AI, digital transformation, or related fields.
- Demonstrated success leading enterprise AI initiatives from strategy through implementation and adoption, resulting in measurable business outcomes.
- Experience designing, implementing, and overseeing enterprise-scale technology architectures and production solutions.
- Experience leading software development teams and delivering technology solutions in complex organizational environments.
- Strong product management experience, including roadmap development, prioritization, stakeholder engagement, and lifecycle management.
- Experience developing and executing enterprise data strategies in partnership with data engineering teams.
- Knowledge of data governance, data quality management, analytics, and AI model evaluation practices.
- Experience establishing AI governance frameworks, policies, standards, and best practices.
- Demonstrated ability to influence executive leadership, lead organizational change, and build strong partnerships across business and technology functions.
- Licenses/Certifications:
- Preferred professional certifications such as Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Azure Solutions Architect Expert, or other comparable cloud, AI, data, or enterprise architecture certifications.
- Software:
- Enterprise AI platforms and tools, including Azure AI Services, Microsoft 365 Copilot, generative AI, and agentic AI technologies.
- Microsoft technology ecosystem and cloud services.
- Data platforms and analytics tools, including Snowflake and Power BI.
- Enterprise applications, CRM platforms, and integration technologies, including Salesforce and related tools.
- ERP systems, with Deltek Vantagepoint experience preferred.
- Microsoft Suite Products (e.g. Outlook, Excel, Word, PowerPoint, SharePoint, OneDrive, etc).
Who We Are:
WSB is a design and consulting firm specializing in Transportation, Community Planning, Environmental, Energy, Municipal, and Construction services. We provide solutions for the places, spaces and systems that support our lives. We offer services in more than 50 complementary areas across the nation.
Full time WSB employees qualify for competitive compensation and benefits package, including medical insurance, dental insurance, vision insurance, life insurance, company paid short-and long-term disability, 401K with match, paid time off, paid holidays, tuition reimbursement, and opportunities for professional growth and development. Part time employees can participate in the 401K plan and applicable earned paid leave.
As an EEO/Affirmative Action Employer, all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, veteran or disability status.
WSB strives to ensure that its careers website is accessible to all. If you need assistance completing your online application, please email [email protected].
As an Equal Opportunity Employer, WSB is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans. If you need a reasonable accommodation to assist with your job search or application for employment, please e-mail us at [email protected]. In your e-mail, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Notice to Third Party Agencies: WSB does not accept unsolicited resumes from third party recruiting firms. Absent a signed Service Agreement by WSB, we reserve the right to pursue and hire any unsolicited candidates without financial obligation to recruiters or agencies.
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At WSB, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
WSB AI Hiring
WSB has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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