Interested in this AI/ML Engineer role at Private Health Management?
Apply Now →Skills & Technologies
About This Role
AI Infrastructure \& Operations Engineer
Location: Remote (U.S.)
Reports To: Juan Sandoval\-Tobias
About Private Health Management
Private Health Management (PHM) supports people with serious and complex medical conditions, helping them obtain the best possible medical care. We guide individuals and families to top specialists, advanced diagnostics, and personalized care. Trusted by healthcare providers and businesses, PHM offers independent, science\-backed insights to help clients make informed decisions and access the best care.
About the Role
PHM is building and scaling Companion, an AI\-enabled clinical platform operating in a high\-trust healthcare environment where reliability, observability, and security are foundational requirements. The platform includes headless AI agents designed to support clinical and operational professionals by acting as intelligent workstations that integrate with enterprise applications and workflows.
The AI Infrastructure \& Operations Engineer will operationalize the platform so it runs reliably at production scale, helping ensure the systems behind Companion are observable, recoverable, secure, and maintainable as adoption grows.
This role sits at the intersection of Kubernetes operations, AI platform reliability, observability engineering, and operational security. You will help evolve and maintain the Azure\-based infrastructure stack while partnering closely with technology leadership, AI architects, and security stakeholders. This is a high\-ownership role for someone who thrives in fast\-moving environments, is comfortable operating with incomplete information, and enjoys building operational discipline around emerging AI systems.
What You'll Accomplish
- Establish operational reliability for Companion across AKS infrastructure, AI agent workloads, monitoring systems, and deployment pipelines.
- Build meaningful observability practices that help PHM understand platform behavior, usage trends, and operational risks before they become incidents.
- Create sustainable operational hygiene around patching, CVE remediation, secrets rotation, dependency management, and cloud maintenance cycles.
- Strengthen platform resilience, documentation, and operational processes so the environment can scale without relying on tribal knowledge.
How You'll Spend Your Days
Operate and Improve Platform Reliability
- Monitor and maintain AKS infrastructure, AI agent workloads, deployment pipelines, and support Azure services.
- Investigate incidents, troubleshoot production issues, and improve platform resilience through better operational patterns and tooling.
- Support release operations and help ensure deployments remain stable, observable, and recoverable.
Build Observability and Operational Insight
- Develop dashboards, alerts, logging patterns, and operational baselines using Azure Log Analytics and Application Insights.
- Identify system trends, performance bottlenecks, and emerging operational risks across infrastructure and AI workloads.
- Improve visibility into AI agent behavior, enterprise workflow integrations, latency patterns, and system health under real user load.
Strengthen Security and Operational Hygiene
- Maintain operational cadence for dependency updates, CVE remediation, image signing, secrets rotation, and cluster patching.
- Support security\-first infrastructure practices across Kubernetes, CI/CD pipelines, and Azure environments.
- Partner with security and engineering stakeholders to maintain compliance\-aware operational practices in a HIPAA\-regulated environment.
Collaborate Across a Small, High\-Ownership Team
- Work closely with technology leadership, platform engineers, security stakeholders, and AI architects to evolve the operational maturity of Companion.
- Contribute documentation, operational runbooks, and shared knowledge that reduce platform fragility over time.
- Help establish practical operational patterns for AI systems where industry best practices are still emerging.
What You Bring to the Table
Required
- Strong hands\-on Kubernetes operations experience, including troubleshooting workloads, admission controllers, cluster networking, and production incidents.
- Experience supporting cloud\-native infrastructure in Azure environments, particularly AKS and related operational tooling.
- Demonstrated strength in monitoring, observability, and incident response using structured logging and metrics platforms.
- SRE mindset with experience handling on\-call responsibilities, operational prioritization, and post\-incident analysis.
- Comfort operating in fast\-moving environments with incomplete documentation, evolving processes, and broad ownership areas.
- Strong communication and collaboration skills with the ability to explain technical issues clearly across technical and non\-technical audiences.
Nice to Have
- Experience with CI/CD pipeline tooling including GitHub Actions, Kaniko, cosign, image signing, or Actions Runner Controller.
- Familiarity with Infrastructure as Code practices using Bicep or Azure resource automation tooling.
- Exposure to HIPAA, SOC2, or other compliance\-aware operational environments.
- Experience supporting AI or LLM\-backed applications in production environments.
Compensation
The target base salary for this position is $120000 \- $140000
This base salary is only a part of a total compensation package that also includes health/dental/vision benefits, annual cash incentive program, 401k with match, flexible PTO, PHM for PHM — our services for you and your dependents — and other benefits. Individual pay may vary from the target range as several factors including market forces, experience, location, disparities in market data, and other relevant business considerations may all factor into final compensation.
Location
This is a remote role requiring that you live in and physically perform all work in the United States.
Next Steps
Private Health Management is a remote company with employees around the United States. We're committed to providing a thoughtful, transparent interview experience and meaningful opportunities to get to know our company, mission, and wonderful teammates through fully remote interviews.
If your application is selected for interviews, you'll hear from a member of our recruiting team to schedule next steps. Interviews will also include the hiring manager, peers, and often an executive from the department.
PHM uses AI\-enabled tools at certain points in the recruiting process to help identify and evaluate top talent; however, all hiring decisions are made by human reviewers.
Have a quick question about the role? Email [email protected] or simply apply here.
Salary Context
This $120K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Private Health Management, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($130K) sits 27% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Private Health Management AI Hiring
Private Health Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.