Interested in this AI/ML Engineer role at Public Consulting Group?
Apply Now →Skills & Technologies
About This Role
Public Consulting Group LLC (PCG) is a leading public sector solutions implementation and operations improvement firm that partners with health, education, and human services agencies to improve lives. Founded in 1986, PCG employs approximately 2,000 professionals throughout the U.S.—all committed to delivering solutions that change lives for the better. The firm is a member of a family of companies with experience in all 50 states, and clients in three Canadian provinces and Europe. PCG offers clients a multidisciplinary approach to meet challenges, pursue opportunities, and serve constituents across the public sector. To learn more, visit www.publicconsultinggroup.com .
PCG is seeking a senior AI Implementation Project Manager to lead the planning, delivery, and client management of AI\-enabled solutions within state and local government health and human services environments. This role owns end\-to\-end project delivery for AI initiatives, driving timelines, managing risk, aligning cross\-functional teams, and serving as the primary point of accountability for client outcomes.
AI Implementation Project Manager
The ideal candidate is a proven technology PM with a track record of delivering complex implementations in public sector or government consulting contexts. They bring structured delivery discipline, comfort working alongside engineering and data science teams, and the credibility to manage senior client relationships. AI fluency is expected, not as an end in itself, but as a foundation for leading delivery teams building real AI systems in production environments.
What This Role Is Not
=========================
- *Not a program manager focused primarily on reporting, coordination, or PMO governance overhead*
- *Not a software engineer or data scientist role, technical depth is a plus, not the primary value*
- *Not an entry\-level project coordinator, this role requires autonomous leadership of client\-facing delivery*
Key Responsibilities
========================
Project Delivery Leadership
-------------------------------
- Own delivery planning, scheduling, and risk management across AI implementation workstreams
- Drive sprint and release planning in collaboration with engineering leads and architects
- Maintain delivery accountability across multiple parallel tracks including requirements, build, integration, UAT
- Identify and escalate delivery blockers; proactively manage scope, timeline, and resource constraints
- Produce and maintain project artifacts: charters, schedules, status reports, and decision logs
Client Management
---------------------
- Serve as primary client\-facing lead; manage senior stakeholder relationships at state agency director level and above
- Facilitate executive\-level status briefings, steering committee updates, and milestone reviews
- Translate client program and policy context into delivery priorities and acceptance criteria
- Navigate client organizational dynamics typical of state government IT modernization engagements
AI Program Oversight
------------------------
- Lead implementation of AI\-enabled workflows, agentic pipelines, and decision\-support systems in HHS contexts
- Coordinate across engineering, solution architects, and QA teams to ensure delivery against technical and functional requirements
- Manage implementation of AI evaluation, validation, and human\-in\-the\-loop processes as defined by program requirements
- Maintain awareness of AI governance, compliance, and explainability requirements relevant to government deployments (FedRAMP, CMS, state data use agreements)
Cross\-Functional Coordination
----------------------------------
- Partner with business analysts to ensure requirements are scoped, traceable, and acceptance\-ready
- Coordinate with subcontractors, vendors, and state agency IT counterparts
- Support proposal and business development activities including staffing plans, delivery approach narratives, and past performance documentation
Required Qualifications
===========================
- 6\+ years of project management experience in technology implementation, with at least 3 years in a client\-facing, billable consulting context
- Demonstrated delivery of complex, multi\-workstream technology programs from inception through go\-live
- Public sector or government consulting experience — state, local, or federal HHS preferred
- PMP certification required (active); Agile certifications (SAFe, CSM) a plus
- Working knowledge of AI systems in production contexts: model behavior, agentic workflows, integration patterns, evaluation
- Strong written and verbal communication; ability to translate technical progress into executive\-level narratives
- Experience managing delivery across matrixed teams including engineers, architects, QA, and subject matter experts
- Proficiency with project management tooling (Jira, ADO, Smartsheet, or equivalent)
Preferred Qualifications
============================
- Experience delivering AI, ML, or data platform programs in government (SNAP, Medicaid, MMIS, or IES modernization a strong plus)
- Familiarity with federal compliance environments: FedRAMP, CMS ARC\-AMPE, or equivalent
- Background in HHS program policy, eligibility systems, or public benefits administration
- Experience with cloud delivery on AWS GovCloud or Azure Government
- Track record in RFP/RFI pursuit support: delivery approach sections, staffing models, work plans
- Comfort managing client environments with significant political, regulatory, or oversight sensitivity
What Strong Candidates Demonstrate
======================================
- A portfolio of multi\-year government technology implementations they personally led to successful outcomes
- Ability to articulate delivery risk and mitigation in concrete, client\-ready terms — not generically
- Enough AI and engineering literacy to challenge timelines, surface hidden dependencies, and evaluate feasibility claims
- History of winning and retaining public sector clients through delivery credibility, not just relationship management
Compensation:
Compensation for roles at Public Consulting Group varies depending on a wide array of factors including, but not limited to, the specific office location, role, skill set, and level of experience. As required by applicable law, PCG provides a reasonable range of compensation for this role. In addition, PCG provides a range of benefits for this role, including medical and dental care benefits, 401k, PTO, parental leave, bereavement leave.
Compensation for roles at Public Consulting Group varies depending on a wide array of factors including, but not limited to, role, skill set, and level of experience. As required by applicable law, PCG provides the following reasonable range of compensation for this role: $69,700\-$125,000\. In addition, PCG provides a range of benefits for this role.
PCG does not sponsor newly hired foreign national workers for work authorization, including H\-1B sponsorship.
EEO Statement:
Public Consulting Group is an Equal Opportunity Employer dedicated to celebrating diversity and intentionally creating a culture of inclusion. We believe that we work best when our employees feel empowered and accepted, and that starts by honoring each of our unique life experiences. At PCG, all aspects of employment regarding recruitment, hiring, training, promotion, compensation, benefits, transfers, layoffs, return from layoff, company\-sponsored training, education, and social and recreational programs are based on merit, business needs, job requirements, and individual qualifications. We do not discriminate on the basis of race, color, religion or belief, national, social, or ethnic origin, sex, gender identity and/or expression, age, physical, mental, or sensory disability, sexual orientation, marital, civil union, or domestic partnership status, past or present military service, citizenship status, family medical history or genetic information, family or parental status, or any other status protected under federal, state, or local law. PCG will not tolerate discrimination or harassment based on any of these characteristics. PCG believes in health, equality, and prosperity for everyone so we can succeed in changing the ways the public sector, including health, education, technology and human services industries, work.
Salary Context
This $69K-$125K 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 Public Consulting Group, 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 ($97K) sits 55% below the category median. Disclosed range: $69K to $125K.
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.
Public Consulting Group AI Hiring
Public Consulting Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $125K - $125K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.