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About This Role
Recognized as a “Best Place to Work Modern Healthcare” – Join a team where people come first. At Vital Care, we are committed to creating an inclusive, growth\-focused environment where every voice matters.
Vital Care is the premier pharmacy franchise business with franchises serving a wide range of patients, including those with chronic and acute conditions. Since 1986, our passion has been improving the lives of patients and healthcare professionals through locally\-owned franchise locations across the United States. We have over 100 franchised Infusion pharmacies and clinics in 35 states, focusing on the underserved and secondary markets. We know infusion services, and we guide owners along the path of launch, growth, and successful business operations.
What we offer:
- Comprehensive medical, dental, and vision plans, plus flexible spending, and health savings accounts.
- Paid time off, personal days, and company\-paid holidays.
- Paid Paternal Leave.
- Volunteerism Days off.
- Income protection programs include company\-sponsored basic life insurance and long\-term disability insurance, as well as employee\-paid voluntary life, accident, critical illness, and short\-term disability insurance.
- 401(k) matching and tuition reimbursement.
- Employee assistance programs include mental health, financial and legal.
- Rewards programs offered by our medical carrier.
- Professional development and growth opportunities.
- Employee Referral Program.
*Job Summary:*
As a Solutions Architect, you will serve as a key technical leader responsible for designing, validating, and guiding the implementation of secure, scalable, and compliant technology solutions that support business objectives and patient care. You will collaborate closely with business, clinical, security, and engineering teams to translate requirements into well\-architected solutions that align with enterprise standards and healthcare regulations.
*Duties/Responsibilities:*
Solution Architecture and Design:
- Design end\-to\-end application, integration, data, and cloud architectures that meet functional, non\-functional, security, scalability, and compliance requirements.
- Develop and maintain architecture diagrams, solution blueprints, and technical design documentation.
- Evaluate and recommend technologies, platforms, and patterns aligned with enterprise architecture standards.
Security, Compliance, and Risk Management:
- Incorporate security\-by\-design principles including identity management, access controls, encryption, audit logging, and data minimization.
- Partner with security and compliance teams to support audit readiness and regulatory obligations.
Collaboration and Communication:
- Collaborate with business, clinical, and technology stakeholders to understand needs and translate them into technical solutions.
- Communicate architectural decisions, risks, and trade\-offs clearly to technical and non\-technical audiences.
- Participate in Agile ceremonies and provide architectural guidance throughout delivery lifecycles.
Reliability and Operational Excellence:
- Design systems for high availability, resiliency, monitoring, disaster recovery, and business continuity.
- Ensure solutions support dependable clinical and operational workflows.
Documentation and Knowledge Sharing:
- Document architecture decisions, standards, and reference designs to support consistency and long\-term maintainability.
- Mentor engineers and teams on architectural best practices and healthcare compliance considerations.
Application Implementation and Development:
- Implement the evolution of enterprise applications, ensuring solutions are secure, scalable, maintainable, and aligned with business and clinical workflows.
- Design and implement AI\-enabled solutions, including GenAI, machine learning services, intelligent automation, and decision\-support capabilities integrated into operational systems.
- Architect, develop and oversee robotics and automation solutions, including RPA, workflow orchestration, and bot\-driven processes, to improve efficiency, accuracy, and scalability across clinical, operational, and administrative functions.
- Implement a standardized service\-layer architectures, including APIs, microservices, event\-driven patterns, and middleware, to enable reusable, loosely coupled systems and partner integrations.
- Lead and implement data integration strategies, including real\-time and batch integrations, data pipelines, interoperability frameworks, and secure exchange between clinical, operational, and analytics platforms.
- Ensure all application implementations incorporate security\-by\-design, privacy controls, audit logging, and compliance with healthcare regulatory standards.
- Support modernization initiatives by refactoring or replacing legacy applications with cloud\-native, API\-driven, and AI\-ready architectures, aligned with enterprise standards.
*Required Skills/Abilities:*
- Strong understanding of application architecture, integration patterns, cloud platforms (preferably Microsoft Azure), and data systems.
- Strong knowledge of Azure identity and security services, including Microsoft Entra ID, Conditional Access, Privileged Identity Management (PIM), Key Vault, and Managed Identities.
- Azure networking and connectivity expertise (VNets, subnets, NSGs, Private Link, ExpressRoute/VPN, DNS) with an emphasis on secure hybrid architectures.
- Experience with infrastructure as code and automation, integrated with CI/CD pipelines (Azure DevOps and/or GitHub Actions).
- Experience designing AI/ML and GenAI solutions on Azure (Azure AI Services, Azure Machine Learning, and/or Azure OpenAI), including model evaluation, prompt/system design patterns, and integration into business workflows.
- Ability to assess risk, make sound architectural decisions, and balance innovation with stability.
- Excellent communication, documentation, and stakeholder collaboration skills.
- Continuous learner with a disciplined, mission\-focused mindset appropriate for healthcare technology environments.
*Education and Experience:*
- Bachelor’s or master’s degree in computer science, information technology, engineering, or a related field.
- 7\+ years of experience in software engineering, systems design, or technical architecture roles, including experience in regulated or healthcare environments.
*Physical Requirements:*
- Sitting: Prolonged periods of sitting are typical, often for the majority of the workday.
- Keyboarding: Frequent use of a keyboard for typing and data entry.
- Reaching: Occasionally reaching for items such as files, documents, or office supplies.
- Fine Motor Skills: Precise movements of the fingers and hands for tasks like typing, using a mouse, and handling paperwork.
- Visual Acuity: Good vision for reading documents, computer screens, and other detailed work
Be part of an organization that invests in you! We are reviewing applications for this role and will contact qualified candidates for interviews.
Vital Care Infusion Services is an equal\-opportunity employer and values diversity at our company. We do not discriminate on the basis of color, race, sex, age, religion, national origin, disability, genetic information, gender identity, sexual orientation, veterans’ status, or any other basis protected by applicable federal, state, or local law.
Vital Care Infusion Services participates in E\-Verify.
This position is full\-time and remote. The salary range for this position is $115,000 \- $135,000\.
Salary Context
This $115K-$135K range is in the lower quartile 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 Vital Care Infusion Services, LLC, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $115K to $135K.
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.
Vital Care Infusion Services, LLC AI Hiring
Vital Care Infusion Services, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $135K - $135K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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