Interested in this AI/ML Engineer role at ABI Home Health Care Agency?
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AI Transformation – Finance \& Strategy Analyst
COMPANY DEFINITION
At TEAM Services Group, we believe in empowering individuals to live life on their terms. As a
national leader in household employment and home care solutions, we champion the
self\-directed care model, giving seniors and individuals with disabilities the freedom to choose
the caregivers and service providers who best fit their needs. Backed by General Atlantic, we
are on a mission to transform home care with compassion, innovation, and a people\-first
approach.
In 2024, TEAM NY joined the TEAM family, bringing its expertise in coordinating personal care
for thousands of Chinese\-American, dual\-eligible Medicare\-Medicaid patients in New York City.
As TEAM’s largest acquisition, TEAM NY is highly visible within the portfolio and is pursuing
ambitious growth targets. We like to win, but we also have fun doing it: we have a close\-knit
culture and prioritize having fun while doing good for the community. The work is mission driven,
and our team genuinely cares to make a difference in our community and beyond.
We are looking for a driven, dynamic teammate to help scale TEAM NY to the next level. This
role is an opportunity to help shape how AI gets deployed across a growing business while also
contributing to the strategy, finance, and operational work that drives performance. If you thrive
in a fast\-paced, mission\-driven environment where winning and having fun go hand in hand, you
will fit right in.
ROLE SUMMARY
In this newly created position, you will help drive TEAM NY’s AI transformation agenda while
accelerating broader strategy and growth priorities. Reporting into the finance team, this is an
AI\-focused builder role embedded within the business, responsible for identifying high\-leverage
workflow opportunities and turning them into practical AI tools, automations, and workflows that
improve decision\-making, increase organizational efficiency, and strengthen the support our
patients receive.
This is an outstanding opportunity for an ambitious early\-career analyst to work at the
intersection of AI, strategy, growth, and finance inside a growing company, with direct exposure
to senior leadership across the brand and broader platform. You will take on meaningful
ownership from day one and play a central role in implementing AI solutions that improve how
teams work, how leaders make decisions, and how the business scales. If you thrive in a
dynamic, fast\-paced environment and want to build practical solutions that drive real business
outcomes, this is the role for you.
WHAT YOU’LL DO O
● Build AI\-native dashboards, internal tools, and workflows that improve visibility and make
data more actionable across finance, growth, strategy, and operational use cases
● Automate recurring reports, analyses, and business processes to reduce manual work,
improve scalability, and accelerate execution
● Partner with leaders and general managers across the business to identify high\-value
transformation opportunities and turn them into scalable AI and automated solutions
● Leverage large datasets to build practical AI\-enabled tools, analyses, and workflows
● Drive recommendations on how internal data, reporting, and business logic are
structured to support more advanced AI and analytics use cases
● Help shape the future of home care by using data, AI, and analytics to identify trends,
support the shift to value\-based care, and drive better patient outcomes
WHAT YOU’LL BRING
● 1\-3 years of experience in either:
o A business\-oriented role such as investment banking, consulting, or corporate
finance, with direct hands\-on experience building and deploying AI, automation,
or workflow tools in a real business environment
o An implementation\-oriented role such as forward deployed engineering, AI
implementation, or AI consulting, with experience translating business needs into
practical AI solutions in close partnership with operators and business leaders
● Direct experience in building AI and automation tools, ideally including internal tools,
dashboards, or websites that help teams interpret data and operate more effectively
● Strong business judgment and the ability to work directly with operators and business
leaders to identify problems, translate needs into practical solutions, and drive adoption
● Familiarity with modern AI development workflows and tools, including prompt\-based
systems, agent workflows, and coding tools
● Strong understanding of databases, SQL, and Snowflake, including how data should be
structured and used to support practical AI, analytics, and workflow solutions
● A builder mindset and a get\-things\-done mentality, with the ability to identify
opportunities, create solutions, and drive work forward with a high degree of ownership
● A collaborative, low\-ego approach, strong communication skills, and the ability to move
fluidly between hands\-on execution and higher\-level problem solving
BENEFITS
● Company\-sponsored medical plan for employees
● 401(k) participation after 3 months of continuous service
● Learning development program
● Employee assistance program
● In addition to accrued vacation time and sick time, TEAM NY recognizes 9 paid holidays.
LOCATION
● 2\-3 days per week: Midtown Manhattan WeWork.
● 1\-2 days per week: Flushing, New York (\~30 to 45 minutes from Manhattan). Our
headquarters and operators are located here. We love working alongside them!
Pay: $100,000\.00 \- $120,000\.00 per year
Experience:
- Building AI and automation tools: 1 year (Required)
Work Location: In person
Salary Context
This $100K-$120K range is in the lower quartile 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 ABI Home Health Care Agency, 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 in Demand for This Role
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. This role's midpoint ($110K) sits 39% below the category median. Disclosed range: $100K to $120K.
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.
ABI Home Health Care Agency AI Hiring
ABI Home Health Care Agency has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Flushing, NY, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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