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About This Role
Job Family:
Data Science Consulting Travel Required:
Up to 10% Clearance Required:
Ability to Obtain Public TrustAbout our AI and Data Capability Team
Our consultants on the AI and Data Analytics Capability team help clients maximize the value of their data and automate business processes. This high\-performing team works with clients across the full data lifecycle, from data strategy and architecture to data warehousing and lakehouse design, to data engineering and querying, to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence, as well as intelligent automation.
We help clients design, build, and operationalize modern data warehouses and analytics ecosystems that support trusted reporting, advanced insights, and scalable AI. Our services enable clients to define an information strategy, deliver mission\-critical insights and data\-driven decision making, reduce cost and complexity, increase trust in data, and improve operational effectiveness.
What You Will Do:
- Design and lead AI/ML and analytics solutions using best\-in\-class tools and platforms.
- Translate business challenges into actionable use cases and scalable data and AI products and services.
- Translate complex business challenges into actionable AI/ML and data solutions using platforms such as Snowflake, Databricks, Azure, AWS, and GCP.
- Lead client engagements from strategy through implementation, ensuring delivery excellence and measurable outcomes.
- Mentor and lead multidisciplinary teams including scientists, engineers, and consultants.
- Lead data warehouse and or lakehouse initiatives, including requirements definition, architecture, modeling, implementation, and production readiness.
- Design and implement scalable data warehouse patterns, including dimensional modeling (star/snowflake), curated data marts, semantic layers, and governed self\-service analytics
- Direct development of ETL/ELT pipelines, orchestration, and automation using modern tooling and engineering practices
- Establish and mature data operationalization capabilities (e.g., performance tuning and cost optimization, release management and incident support processes, data quality rules and monitoring).
- Partner with client stakeholders to align warehousing work with data governance practices, access controls, and operational support models.
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 or DoD "PUBLIC TRUST".
- Minimum FIVE (5\) years of experience delivering technical solutions and programs to public sector organizations.
- Experience in data and AI system development, with a proven ability to design scalable architectures and implement reliable models.
- Demonstrated experience leading data warehouse design and implementation, including data modeling, ingestion patterns, and production operations/
- Strong knowledge of data warehousing concepts (e.g., dimensional modeling, analytics\-ready data structures, CDC patterns, performance tuning, and cost controls for cloud platforms).
- Experience defining and implementing operating models for data platforms, including support processes, SLAs, monitoring, and continuous improvement.
- The knowledge and interest to remain current on emerging trends and techniques in the fields of data science and Artificial Intelligence.
- Strong communication skills to bridge technical and business worlds.
- During peak periods, 50% travel to north east client site, ie. during requirements gathering, UAT, implementation/go live, etc.
What Would Be Nice To Have:
- Experience with MLOps and CI/CD pipelines for AI/ML deployment.
- Experience implementing cloud data warehouse and lakehouse solutions using Snowflake and or Databricks, including security, governance, and performance optimization.
- Demonstrated work experience within the public sector.
- Familiarity with data privacy regulations (GDPR, CCPA) and ethical AI frameworks.
- Advanced Degree (Master’s or Ph.D.) in Data Science, Computer Science, AI, or related field.
- Experience with API development and integration for data services.
- Experience supporting business development including RFP/RFQ/RFI responses involving data science / analytics.
The annual salary range for this position is $141,000\.00\-$235,000\.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 $141K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $141K to $235K.
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.
Guidehouse AI Hiring
Guidehouse has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, Austin, TX, US. Compensation range: $124K - $235K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 3,823 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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