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##### Healthcare AI Deployment \& Customer Expansion Lead
##### Company: HireNow Staffing (Direct Placement Partner)
##### HireNow Snapshot
##### HireNow Staffing is actively recruiting a highly capable Healthcare AI Deployment \& Customer Expansion Lead to join one of our valued client partners as it expands its AI voice technology into the U.S. healthcare market.
##### *This is a high\-ownership opportunity for an implementation and customer success professional who can take complete responsibility for the* *post\-sale customer journey—from signed agreement through configuration, deployment, adoption, retention, and account expansion**.*
##### *As an early U.S. operations hire, this individual will build the deployment and customer operations function rather than inherit an established playbook. Reporting directly to executive leadership, the selected candidate will develop repeatable onboarding processes, independently configure customer deployments, establish strong healthcare customer relationships, and ultimately build and lead a small U.S. operations team.*
##### *Success will be measured heavily by* *net revenue retention, deployment performance, customer* expansion, and the ability to create a scalable U.S. implementation model.
##### Key Responsibilities
- Own the complete post\-sale lifecycle for U.S. healthcare customers from signed contract through production deployment and ongoing account growth.
- Configure customer environments and manage implementation activities required to successfully launch AI voice solutions into production.
- Drive time\-to\-go\-live while maintaining high\-quality and successful customer deployments.
- Become the primary post\-sale relationship owner across customer success, support, implementation, and account management.
- Develop deep product expertise sufficient to independently configure deployments and troubleshoot implementation challenges.
- Work with AI integrations, workflow configurations, no\-code tooling, and related technologies required for customer deployments.
- Establish strong relationships with healthcare customers and translate operational requirements into effective product configurations.
- Drive retention, renewals, upsell, and cross\-sell opportunities with net revenue retention as a core performance metric.
- Own an individual expansion and upsell target.
- Identify customer risks early and coordinate solutions that protect retention and long\-term account value.
- Build and document a repeatable U.S. onboarding and deployment framework covering configurations, workflows, implementation standards, and best practices.
- Partner directly with Sales, Product, Engineering, and executive leadership to improve customer outcomes and U.S. market execution.
- Help scale the U.S. operations function and eventually hire and lead a small customer operations team.
##### Required Qualifications
- 3–5 years of experience in customer\-facing implementation, deployment, customer success, solutions delivery, or a closely related post\-sale function.
- Demonstrated ownership of customer implementations from handoff through successful production launch.
- Experience directly managing customer relationships after the sale.
- Strong ability to learn technical products and independently manage product configurations.
- Demonstrated ability to coordinate multiple customer deployments, priorities, and deadlines simultaneously.
- Strong analytical and problem\-solving capabilities with an ability to identify operational issues and develop practical solutions.
- Excellent customer\-facing written and verbal communication skills.
- Ability to collaborate effectively with Sales, Product, Engineering, and executive stakeholders.
- Comfortable operating without established processes and creating structure in an early\-stage environment.
- Ability to work in a hybrid environment in New York City.
- Authorized to work in the United States without current or future employment sponsorship.
##### Preferred Qualifications
- Experience implementing AI, voice AI, SaaS, healthcare technology, or workflow automation products.
- Experience supporting healthcare organizations, medical practices, providers, or healthcare technology customers.
- Understanding of healthcare scheduling, inbound patient communications, patient engagement, or provider workflows.
- Previous experience working at a Series A or similarly early\-stage technology company.
- Experience with AI integrations, APIs, workflow tools, or no\-code configuration platforms.
- Demonstrated success improving customer retention or net revenue retention.
- Experience identifying and closing customer expansion, upsell, or cross\-sell opportunities.
- Experience developing onboarding or implementation playbooks from the ground up.
- Previous experience hiring, mentoring, or leading customer operations professionals.
- Stable employment history demonstrating increasing ownership within implementation, deployment, or customer success.
##### HireNow Package
##### Compensation:$110,000–$180,000 annually
##### Employment Type: Full\-Time \| Direct Placement
##### Work Location: Hybrid – New York City, New York
##### Experience: 3–5 years of directly relevant experience.
##### Growth Opportunity: Opportunity to build the U.S. post\-sale operations function and grow into leadership of an approximately three\-person team as the customer base expands.
##### Work Authorization: Applicants must be authorized to work in the United States without current or future sponsorship.
##### Visa Sponsorship: None available.
##### HireNow Checklist
##### HireNow Staffing is recruiting a Healthcare AI Deployment \& Customer Expansion Lead who:
- Brings 3–5 years of customer\-facing implementation, deployment, customer success, or related post\-sale experience.
- Can personally own the journey from signed customer through configuration, implementation, production launch, adoption, retention, and expansion.
- Has the technical aptitude to develop deep product knowledge and independently configure customer deployments.
- Understands that customer success includes commercial accountability for retention and expansion, not simply relationship management.
- Can build processes and operational structure where no established U.S. deployment playbook currently exists.
- Communicates confidently with customers while collaborating effectively with technical and commercial teams.
- Is comfortable operating with significant individual accountability in an early\-stage environment.
- Can work hybrid in New York City without requiring employment sponsorship.
##### HireNow Standout Candidates
##### Candidates will receive the strongest consideration if they demonstrate:
- Direct implementation or deployment experience with AI, voice AI, healthcare SaaS, or technically complex software products.
- Proven ownership of customer deployments from contract handoff through production go\-live.
- Measurable success improving net revenue retention, renewals, expansion revenue, or customer adoption.
- Experience personally identifying and closing upsell or cross\-sell opportunities.
- Strong knowledge of healthcare operational or patient\-communication workflows.
- Experience creating implementation and onboarding processes from a blank slate.
- Ability to operate comfortably across technical configuration, customer relationships, commercial expansion, and operational strategy.
- Experience helping build an early\-stage customer operations organization and eventually developing a team.
##### HireNow Disqualifiers
##### The following will prevent candidates from moving forward:
- Jumpy resumes will not be accepted or interviewed.
- Less than three years of relevant customer\-facing implementation, deployment, or customer success experience.
- Experience limited to customer support without meaningful implementation, deployment, or customer ownership.
- No demonstrated experience managing customers through post\-sale implementation or production launch.
- Limited comfort working with technical products, configurations, or implementation workflows.
- No willingness to own retention and customer expansion responsibilities.
- Candidates seeking a highly structured environment with established processes rather than a greenfield operational build.
- Candidates unable to meet the New York City hybrid work requirement.
- Applicants requiring current or future visa sponsorship.
- Candidates who do not meet the core qualifications.
##### HireNow Staffing Disclaimer
##### HireNow Staffing is acting as a direct placement partner for this Healthcare AI Deployment \& Customer Expansion Lead opportunity. All candidate information is handled confidentially and evaluated against defined requirements. This job description outlines the general scope of responsibilities and qualifications. Duties may evolve based on client needs and business growth. Only candidates meeting the core qualifications will be considered for interview. Client\-specific information will be shared only with qualified candidates during the interview process.
##### https://www.careers\-page.com/hirenow\-staffing\-inc/job/3W8W73R8
Salary Context
This $110K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HireNow Staffing, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 33% below the category median. Disclosed range: $110K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HireNow Staffing AI Hiring
HireNow Staffing has 3 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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