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About This Role
CentralSquare Technologies is Hero-Grade. As the trusted provider of public sector software in North America, more than 8,000 agencies rely on our comprehensive, cloud-based platform to manage critical operations – from dispatch to records, permitting to payroll. We serve with purpose and stand together with our heroes, committed to supporting the public sector with software built for impact.
What We’re About
At CentralSquare, we don’t just build software - we power public servants and uplift communities with Hero-Grade Technology. Every line of code, every feature we deliver helps heroes across North America protect, serve, and save lives. When you join us, you become part of a mission-driven team creating technology that makes communities safer and stronger.
Your Growth Matters. We believe heroes deserve opportunities to rise. That’s why we invest in your career with mentorship, learning programs, and clear paths for advancement. If you’re motivated, there’s no limit to how far you can go.
Your Commitment Deserves Reward. We offer competitive compensation and a benefits package designed to support your life inside and outside of work—tuition reimbursement, parental leave, paid volunteer hours, and unlimited PTO. Plus, our flexible work environment gives you the freedom to balance your heroic work with personal well-being, whether you’re in the office or remote.
Join us and help build the tools that power real-life heroes. Together, we make a difference.
The Opportunity
CentralSquare Technologies is hiring a Lead Project Manager of AI Initiatives to serve as the dedicated project management resource for the company's growing portfolio of AI programs. As CentralSquare accelerates its AI-first transformation (spanning customer support AI, managed service innovation, and enterprise-wide AI adoption), this role provides the operational discipline, tracking rigor, and cross-functional coordination required to move initiatives from concept to measurable outcome.
This role sits within the Enterprise PMO under the Director of Business Operations and partners closely with the VP of Business Operations, who leads enterprise AI strategy. The Lead PM will not own AI strategy but will ensure that AI initiatives are governed, tracked, and reported with the same discipline applied to all strategic programs across the portfolio.
Key Responsibilities
AI Initiative Governance
- Maintain a current inventory of all active AI initiatives across the enterprise, including ownership, status, milestones, and KPIs.
- Establish and run a consistent governance cadence for AI programs: weekly check-ins, milestone reviews, and escalation processes.
- Track initiative health and surface risks, blockers, and dependencies before they become delivery failures.
- Ensure AI initiative owners have clear charters, defined success criteria, and accountability structures in place.
Planning & Execution Support
- Partner with AI initiative owners to build out project plans, timelines, and resource maps for new and in-flight programs.
- Translate high-level AI strategy objectives into executable work plans with clear owners, dates, and deliverables.
- Facilitate planning sessions, working sessions, and retrospectives across AI workstreams.
- Identify and resolve cross-initiative dependencies and conflicts within the AI portfolio.
Reporting & Executive Visibility
- Produce weekly and monthly AI portfolio status reports for the VP of Business Operations and ELT, structured as the AI equivalent of the EPMO portfolio dashboard including initiative health, milestone progress, and ROI indicators such as deflection rates, productivity gains, and cost impact.
- Maintain dashboards that track AI initiative progress, adoption metrics, and outcome KPIs including deflection rates, CSAT, and agent productivity gains.
- Prepare briefing materials for Vista Value Creation Team updates and executive steering reviews.
Stakeholder Coordination
- Act as the connective tissue between AI initiative owners, the Enterprise PMO, and the VP of Business Operations.
- Coordinate across Product, Engineering, Customer Support, Managed Services, and other functions to ensure AI programs have the cross-functional alignment they need to execute.
Support the AI Council cadence: prepare agendas, capture decisions and action items, and track follow-through. *
CentralSquare Technologies: Where Mission Meets Innovation
CentralSquare Technologies is proud to be an Equal Opportunity Employer. We are committed to fostering a workplace that is inclusive, respectful, and free from discrimination—where all individuals are valued, supported, and provided equal opportunity to succeed.
We place a strong emphasis on supporting military veterans and their spouses and recognize the leadership, discipline, resilience, and mission-oriented mindset they bring to the workforce. The skills developed through military service—such as accountability, teamwork, adaptability, and the ability to perform under pressure—are highly valued at CentralSquare and directly contribute to our success.
In addition to our commitment to veterans, CentralSquare Technologies welcomes and encourages applicants from all backgrounds. We are committed to equal employment opportunity for all qualified individuals, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law.
Our employees reflect a wide range of experiences, perspectives, and identities, and this diversity strengthens our ability to innovate and serve the public sector, whether through our public safety or public administration businesses. Through our technology, we support public servants and the communities they serve, and we believe our workforce should reflect those communities as well.
If you are seeking an opportunity to contribute to meaningful work that impacts communities nationwide—whether continuing a legacy of service or beginning a new chapter—CentralSquare Technologies invites you to apply.
Your next mission starts here.
Qualifications
- 5+ years of project management experience, including direct ownership of complex, cross-functional programs.
- Demonstrated experience managing technology or software implementation programs — AI, SaaS, or platform projects preferred.
- Strong command of project governance frameworks: charter development, milestone tracking, risk management, and escalation.
- Exceptional communication skills; able to translate technical AI program details into clear executive-level reporting.
- Proficiency with project and portfolio management tools (Azure DevOps, Smartsheet, or similar).
- Comfortable operating in ambiguity and building structure where it does not yet exist.
- Familiarity with AI tooling ecosystems including generative AI platforms, LLM-based tools, and AI vendor landscapes.
- PMP, PMI-ACP, PRINCE2.
- Background in public safety, public administration, or government software is a plus.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At CentralSquare Technologies, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
CentralSquare Technologies AI Hiring
CentralSquare Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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