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About This Role
Min: USD $200,000\.00/Yr. Max: USD $250,000\.00/Yr. Position Overview:
- SCOPE OF ROLE:
The Vice President of Apps, Data \& AI is a senior technology leader responsible for driving the design, development, and continuous improvement of S:US’s application portfolio, data infrastructure, and artificial intelligence capabilities. This role is the organizational authority on the Microsoft Fabric, AI agents, Microsoft Power Platform, process automation, and the full analytics and reporting ecosystem that enables S:US leadership and program staff to make data\-informed decisions at scale.
Reporting to the CIO, the VP will lead a team of engineers, data professionals, and automation specialists, serving as both a hands\-on architect and a strategic partner to Operations, Finance, HR, Clinical Services, and Housing program leadership. The ideal candidate brings a passion for using technology to advance the S:US mission of empowering underserved communities.
- ESSENTIAL DUTIES \& RESPONSIBILITIES:
- Provide strategic and technical leadership for enterprise applications, data platforms, business intelligence, automation, and AI initiatives that support organizational operations and mission delivery.
- Lead the design, development, implementation, integration, and ongoing support of enterprise applications and data solutions.
- Establish and maintain enterprise data governance, security, compliance, and quality standards. Direct the organization's AI, automation, and digital transformation strategy to improve operational efficiency and service delivery.
- Oversee enterprise analytics and reporting platforms to enable data\-driven decision making.
- Lead and develop a high\-performing team of application developers, data engineers, BI developers, and automation specialists.
- Manage technology vendors, software licensing, and strategic partnerships.
- Ensure all technology solutions comply with applicable regulatory, privacy, and security requirements.
- Collaborate with executive leadership, business stakeholders, clinical leadership, and external partners to align technology initiatives with organizational priorities.
- KEY RESPONSIBILITIES:
Applications \& Platform Engineering
- Architect, build, and maintain enterprise applications using Microsoft Power Apps (Canvas and Model\-Driven), Power Pages, and Visual Studio/.NET.
- Lead API design and development using RESTful standards to integrate internal systems with external partners, SaaS applications, and clinical platforms.
- Oversee the full application lifecycle, including requirements gathering, development, testing, deployment, change management, version control, and documentation for current and future enterprise applications.
- Ensure application performance, security, scalability, and accessibility standards are maintained across all enterprise platforms.
Data Platform \& Engineering
- Lead the implementation, governance, and optimization of Microsoft Fabric, including Lakehouses, Data Warehouses, Data Pipelines, Spark Notebooks, and OneLake.
- Design, develop, and maintain enterprise data pipelines integrating SQL Server, EHR systems, housing platforms, HR systems, and other operational data sources.
- Partner with business and operational leaders to develop enterprise data models, semantic layers, and KPI frameworks.
- Establish SQL Server database administration standards, optimize database performance, and oversee enterprise data quality initiatives.
AI, Agentic AI \& Intelligent Automation
- Develop and execute the organization's AI strategy utilizing Microsoft Copilot Studio, Azure OpenAI, and other AI technologies.
- Design, implement, and govern agentic AI solutions that automate complex workflows, referrals, scheduling, and operational decision support.
- Lead the Robotic Process Automation (RPA) program using Power Automate Desktop and cloud\-based workflows to streamline business processes.
- Establish responsible AI governance, including model monitoring, bias mitigation, human oversight, and compliance with applicable federal, state, and local AI regulations.
Analytics \& Business Intelligence
- Administer and govern the enterprise Power BI environment, including semantic models, workspaces, gateways, row\-level security, and certified datasets.
- Develop executive dashboards and enterprise reporting solutions that support organizational performance management.
- Partner with leadership to define, monitor, and publish KPIs across housing, behavioral health, veterans’ services, and developmental disability programs.
- Promote self\-service analytics while maintaining enterprise reporting standards and data integrity.
Data Governance \& Compliance
- Develop and maintain enterprise data governance policies, including data dictionaries, metadata, lineage, ownership, classification, and retention standards.
- Ensure compliance with HIPAA, 42 CFR Part 2, NYC DOHMH, NYS OASAS, HUD, and other applicable regulatory requirements.
- Collaborate with Legal, Compliance, Information Security, and Clinical leadership to govern sensitive organizational data.
- Lead enterprise data stewardship initiatives and facilitate cross\-functional data governance committees.
Leadership \& Project Delivery
- Recruit, mentor, coach, and develop a high\-performing team of technology professionals.
- Lead Agile project delivery by managing Jira backlogs, sprint planning, sprint reviews, retrospectives, and delivery reporting.
- Foster a collaborative, innovative, and mission\-driven technology culture.
- Manage vendor relationships, contracts, software licensing, and service agreements for Microsoft technologies and related platforms.
- Partner with executive leadership to prioritize technology investments and align initiatives with strategic organizational goals.
Benefits Overview: We offer attractive compensation with comprehensive benefits including Medical/Dental/Prescription/Vision/Life Insurance; 403(b); Credit Union; FSAs; Short\-and\-Long\-Term Disability; Transportation Plan; Generous Paid Vacations and Holidays’
Qualifications:
REQUIRED QUALIFICATIONS* Bachelor’s degree in computer science, Information Systems, Data Science, or a related field; equivalent combination of education and experience considered.
- 8\+ years of progressive technology experience, with 3\+ years in a senior leadership role (Director or above).
- Deep, hands\-on expertise architecting and delivering Microsoft Fabric solutions (lakehouses, pipelines, semantic models, and Real\-Time Analytics).
- Proficiency in API development (REST/GraphQL) and experience with Visual Studio and .NET ecosystems.
- Advanced SQL Server skills: T\-SQL, stored procedures, query optimization, and database design.
- Proven track record leading RPA programs with Power Automate Desktop or comparable tools.
- Experience designing and deploying AI agents and agentic AI workflows (Copilot Studio, Azure OpenAI, or equivalent).
- Experience with the Microsoft Power Platform: Power Apps, Power Automate, Power BI, and Power Pages.
- Strong knowledge of data governance principles, data modeling, and compliance in regulated environments like HIPAA.
- Demonstrated success leading Agile teams using Jira or a comparable project management platform.
*PREFERRED QUALIFICATIONS** Master’s degree in technology, data science, or management discipline.
- Microsoft certifications: Fabric Analytics Engineer, Power BI Data Analyst, Azure AI Engineer, Power Platform Solution Architect.
- Experience with EHR systems
- Experience with NYC government data systems, ePACES, AWARDS/Foothold, or HUD reporting platforms.
- Experience with GitHub Actions, Azure DevOps, or comparable CI/CD pipelines.
Company Overview:
S:US IS AN EQUAL OPPORTUNITY EMPLOYER
Join a team of employees who care about the wellbeing of others. We believe in fostering a culture built on our core values: respect, integrity, support, maximizing individual potential and continuous quality improvement. From health and wellness resources to generous PTO, professional development, and more, explore all that we offer on ourBenefits Page and see how S:US invests in you.
We believe in fostering a culture built on our core values: respect, integrity, support, maximizing individual potential and continuous quality improvement.
All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, age, sexual orientation, national origin, veteran status, or genetic information and including all other statuses protected by Federal, State and Local laws. S:US is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities, including allowance of the use of services animals. To request reasonable accommodation or if you believe such a request was improperly handled or denied, contact the Leave Team at [email protected].
ID: 2026\-18681 Work Location: true
Salary Context
This $200K-$250K range is above the 75th percentile 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 Services For The Underserved, 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. Disclosed range: $200K to $250K.
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.
Services For The Underserved AI Hiring
Services For The Underserved has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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
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