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About This Role
Job Family:
Data Science Consulting Travel Required:
Up to 10% Clearance Required:
Ability to Obtain Public Trust
We are seeking a Senior Consultant AI Strategist to support state agencies in advancing their adoption of AI through practical, mission\-aligned strategies and implementation planning. This role focuses on helping agencies translate emerging AI capabilities into actionable modernization roadmaps that reflect their priorities, constraints, and operating realities. This role will also assist in translating this roadmap into actionable initiatives and technology projects that will operationalize AI in responsible and value\-added manners within mission support and mission delivery functions within the agency.
This is a highly collaborative, solution\-oriented role that sits at the intersection of business strategy, technology, and operations. The ideal candidate is current on AI trends and technologies and can effectively engage both business and technical stakeholders to guide investment decisions, shape use cases, and support sustainable adoption of AI.
What You Will Do:
- Facilitate development of AI strategies and roadmaps aligned to agency mission, priorities, and operational realities
- Advise leadership on AI investment planning, use case prioritization, and value realization
- Lead stakeholder engagements to assess organizational readiness, data maturity, and change considerations
- Translate complex AI concepts into clear, actionable recommendations for business and non\-technical audiences
- Identify and define high\-impact AI use cases across programs, operations, and service delivery
- Support evaluation and selection of AI technologies and solutions, including vendor and platform considerations
- Develop frameworks for responsible AI adoption, including governance, risk, and oversight
- Facilitate workshops and working sessions to build alignment across business, IT, and data teams
- Support early\-stage implementation planning, including operating model, workforce impacts, and adoption strategy
What You Will Need:
- Bachelor’s degree from an accredited college/university
- Based on our contractual obligations, candidate must be located within the United States and US Citizen
- Must be able to obtain and maintain a federal public trust
- 3\+ years of experience in consulting, strategy, or technology advisory roles
- Demonstrated experience supporting AI initiatives
- Strong understanding of key AI concepts and technologies (e.g., LLMs, GenAI, machine learning, automation)
- Experience translating technical capabilities into business\-relevant strategies and actionable plans
- Ability to facilitate discussions with both executive leadership and technical teams
- Experience developing strategy documents, roadmaps, and investment plans
- Strong communication skills with the ability to simplify complex topics for non\-technical audiences
- Proven ability to operate in ambiguous environments and develop practical, solution\-oriented approaches
- Some travel may be required during key project milestones
What Would Be Nice To Have:
- Experience working with state health or human services agencies
- Experience supporting or transforming contact center operations, including use of AI for customer engagement or service delivery
- Experience evaluating and implementing AI solutions for public sector workforce productivity and citizen\-facing services
- Familiarity with responsible AI, governance, and risk management frameworks in a government context
- Experience working in cross\-functional environments involving policy, program, and technology stakeholders
The annual salary range for this position is $113,000\.00\-$188,000\.00\. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
Benefits include:
- Medical, Rx, Dental \& Vision Insurance
- Personal and Family Sick Time \& Company Paid Holidays
- Position may be eligible for a discretionary variable incentive bonus
- Parental Leave and Adoption Assistance
- 401(k) Retirement Plan
- Basic Life \& Supplemental Life
- Health Savings Account, Dental/Vision \& Dependent Care Flexible Spending Accounts
- Short\-Term \& Long\-Term Disability
- Student Loan PayDown
- Tuition Reimbursement, Personal Development \& Learning Opportunities
- Skills Development \& Certifications
- Employee Referral Program
- Corporate Sponsored Events \& Community Outreach
- Emergency Back\-Up Childcare Program
- Mobility Stipend
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1\-571\-633\-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
*Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.*
Salary Context
This $113K-$188K range is below the median 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 Guidehouse, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($150K) sits 31% below the category median. Disclosed range: $113K to $188K.
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.
Guidehouse AI Hiring
Guidehouse has 6 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span New York, NY, US, Chicago, IL, US, Washington, DC, US. Compensation range: $124K - $216K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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