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About This Role
Why USA Clinics Group?
Founded by Harvard\-trained physicians with a vision of offering patient\-first care beyond the hospital settings, we’ve grown into the nation’s largest network of outpatient vein, fibroid, vascular, and prostate centers, with 170\+ clinics across the country. Our mission is simple: deliver life\-changing, minimally invasive care, close to home.
We’re building a culture where innovation, compassion, and accountability thrive. While proud of our growth, we’re even more excited about what’s ahead, and the team we’re building to get there. We look forward to meeting you!
Why You'll Love Working with us:
Rapid career advancement Competitive compensation package
Positive, team\-oriented environment Work with cutting\-ed technology
Make a real impact on patients’ lives Join a fast\-growing, mission\-driven company
Position Summary:
The LLM Engineer serves as the organization's AI technical lead responsible for designing, implementing, and optimizing Large Language Model (LLM) solutions that automate business processes, improve operational efficiency, and support strategic initiatives across all departments. This role partners with business leaders to identify opportunities for AI adoption and develops scalable solutions that drive measurable organizational impact.
Position Details:
- Location: Northbrook, IL
- Schedule: Full\-time, Monday through Friday on\-site.
- Compensation: $85,000 \- $115,000
Key Responsibilities* Design, develop, deploy, and maintain AI\-powered applications, workflows, copilots, agents, and automation solutions using large language models.
- Partner with department leaders to identify business processes that can be improved through AI, automation, and advanced analytics.
- Develop and maintain secure AI infrastructure, prompt libraries, knowledge bases, integrations, and governance standards.
- Build AI solutions that support recruiting, credentialing, revenue cycle, finance, operations, marketing, and clinical support functions.
- Create and maintain documentation, SOPs, technical standards, and best practices for AI implementation across the organization.
- Monitor AI system performance, accuracy, adoption, security, and return on investment.
- Lead AI education, training, and change management initiatives to increase organizational adoption and effectiveness.
- Serve as the organization's subject matter expert on AI technologies, emerging trends, and innovation opportunities.
AI \& Automation Responsibilities* Develop custom GPTs, AI agents, retrieval\-augmented generation (RAG) solutions, and workflow automations that improve operational efficiency.
- Integrate AI platforms with business systems such as ATS, CRM, credentialing, revenue cycle, HRIS, document management, and reporting tools.
- Build automated reporting, data extraction, document processing, and decision\-support solutions using AI technologies.
- Evaluate and implement emerging AI technologies, models, frameworks, and tools to continuously improve business performance.
Requirements
Minimum Requirements* Bachelor's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or related field required.
- Minimum 3\-5 years of software engineering, machine learning, AI development, or automation experience.
- Experience working with Large Language Models (LLMs), prompt engineering, AI APIs, and AI development platforms.
- Proficiency in Python, SQL, APIs, cloud platforms, and workflow automation tools.
- Experience building, deploying, and maintaining production\-grade AI applications and integrations.
Benefits
- Health
- Dental
- Vision
- 401k \& Match
- Paid time off
Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 3,824 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At USA Clinics Group, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $144,405 based on 8 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($100K) sits 31% below the category median. Disclosed range: $85K to $115K.
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.
USA Clinics Group AI Hiring
USA Clinics Group has 3 open AI roles right now. They're hiring across AI/ML Engineer, LLM Engineer, AI Product Manager. Based in Northbrook, IL, US. Compensation range: $100K - $115K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture 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).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
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
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