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About This Role
Join us in pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably.
Our inspiring and caring environment forms a global community that celebrates diversity and individuality. We encourage you to step beyond your comfort zone, offering resources and flexibility to foster your professional and personal growth, all while valuing your unique contributions.
Varian Medical Systems, a Siemens Healthineers company is hiring for an AI LLM Engineer. This role is ideal for experienced engineers specializing in Applied AI, Large Language Models (LLMs), and intelligent automation, with a strong foundation in data platforms such as Snowflake and modern analytics ecosystems like Power BI/Fabric. The position focuses on designing, building, and operationalizing AI driven solutions, including RAG pipelines, enterprise copilots, and agent based automation frameworks, to transform how the Analytics \& Reporting team delivers insights and interacts with data. You will partner with business stakeholders, data engineers, and platform teams to build production grade AI systems that enable natural language interaction, automate complex workflows, and enhance self service analytics at scale. This role is well suited for someone evolving from data engineering, ML engineering, or analytics engineering into AI platform ownership, agentic system design, or enterprise GenAI leadership.
\*\*This role is designed to be onsite in Atlanta, Georgia with some remote/hybrid flexibility\*What You will do:
AI / LLM Engineering \& Agentic Systems
- Design, build, and deploy LLM powered applications using architectures such as Retrieval Augmented Generation (RAG), tool augmented agents, and multi agent workflows
- Develop AI copilots and natural language interfaces over enterprise data platforms (Snowflake, Power BI datasets, Fabric/OneLake)
- Build and orchestrate AI agents using frameworks such as:
+ LangChain / LangGraph
+ Semantic Kernel
+ Microsoft Copilot Studio
+ Azure AI Foundry
- Implement context management strategies (embeddings, vector stores, retrieval optimization, chunking, ranking)
- Design tool integrations enabling agents to query data, trigger workflows, and interact with enterprise systems
- Develop evaluation frameworks for prompt performance, answer quality, hallucination mitigation, and reliability
- Collaborate with platform and governance teams to ensure responsible AI practices, security, and compliance
Applied Machine Learning \& Intelligent Automation
- Build and deploy ML models for forecasting, anomaly detection, and predictive analytics
- Integrate traditional ML with LLM based systems to enable hybrid intelligence workflows
- Develop Python based pipelines for model training, evaluation, and deployment
- Apply prompt engineering, fine tuning strategies, and model orchestration techniques to improve system performance
Data Platform Integration (Snowflake, Fabric, Power BI)
- Enable AI systems to interact with structured data using optimized SQL queries and semantic models
- Design AI ready data layers (feature stores, curated datasets, vector indexes) on Snowflake
- Collaborate with data teams to ensure high quality, well modeled data pipelines that support AI use cases
- Integrate with Power BI/Fabric to deliver solutions with NL querying and automated insights
Automation \& Workflow Orchestration
- Build intelligent automation using Python, APIs, and orchestration tools
- Develop agent driven workflows for:
+ Automated reporting
+ Data quality monitoring
+ Stakeholder Q\&A and decision support
- Integrate AI systems with enterprise tools (ServiceNow, Salesforce, etc.) for end to end automation
Collaboration \& Communication
- Work across business, analytics, and engineering teams to identify high impact AI use cases
- Translate technical AI solutions into clear business outcomes and value propositions
- Present architecture decisions, tradeoffs, and governance considerations to stakeholders
- Contribute to best practices, reusable components, and internal AI knowledge sharing
What You will have:
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related quantitative field
- 4\+ years of experience in AI/ML engineering, data engineering, or advanced analytics roles
- Proven experience building and deploying at least one GenAI/LLM based solution in production or near production
- Strong understanding of LLM architectures, RAG, embeddings, vector databases, and prompt engineering
- Experience designing scalable, production grade systems in enterprise environments
- Strong problem solving and communication skills with the ability to connect AI solutions to business outcomes
Technical Skills
- Python (required) with experience in LLM frameworks and ML libraries
- Deep understanding of RAG pipelines, Prompt Engineering and evaluation, Agent based architectures
- Experience with Azure Open AI/Azure AI Foundry, Databricks or similar platforms
- Strong SQL (Snowflake preferred) with query optimization experience
- Experience integrating AI with structured data systems
- Familiarity with data modeling, ELT pipelines, and data warehousing concepts
- Understanding of model deployment, monitoring, and evaluation in production
What will set you apart:
- Experience building enterprise copilots or conversational AI systems
- Familiarity with Microsoft Fabric, OneLake, and Direct Lake architectures
- Experience with Power Automate, Power Apps, or low code integrations
- Knowledge of multi agent orchestration and workflow design patterns
- Experience in regulated industries (e.g., healthcare, medical devices) with AI governance considerations
\#LS\-OS1
Who we are: We are a team of more than 72,000 highly dedicated Healthineers in more than 70 countries. As a leader in medical technology, we constantly push the boundaries to create better outcomes and experiences for patients, no matter where they live or what health issues they are facing. Our portfolio is crucial for clinical decision\-making and treatment pathways.
How we work: When you join Siemens Healthineers, you become one in a global team of scientists, clinicians, developers, researchers, professionals, and skilled specialists, who believe in each individual’s potential to contribute with diverse ideas. We are from different backgrounds, cultures, religions, political and/or sexual orientations, and work together, to fight the world’s most threatening diseases and enable access to care, united by one purpose: to pioneer breakthroughs in healthcare. For everyone. Everywhere. Sustainably.
To find out more about Siemens Healthineers businesses, please visit our company page here.
The base pay range for this position is:
$93,680 \- $128,810
Factors which may affect starting pay within this range may include geography/market, skills, education, experience, and other qualifications of the successful candidate.
If this is a commission eligible position the commission eligibility will be in accordance with the terms of the Company's plan. Commissions are based on individual performance and/or company performance.
The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan. life insurance, long\-term and short\-term disability insurance, paid parking/public transportation, paid time off, paid sick and safe time.
Equal Employment Opportunity Statement: Siemens Healthineers is an Equal Opportunity and Affirmative Action Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to their race, color, creed, religion, national origin, citizenship status, ancestry, sex, age, physical or mental disability unrelated to ability, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, order of protection status, protected veteran or military status, or an unfavorable discharge from military service, and other categories protected by federal, state or local law.
EEO is the Law: Applicants and employees are protected under Federal law from discrimination. To learn more, click here.
Reasonable Accommodations: Siemens Healthineers is committed to equal employment opportunity. As part of this commitment, we will ensure that persons with disabilities are provided reasonable accommodations.
If you require a reasonable accommodation in completing a job application, interviewing, completing any pre\-employment testing, or otherwise participating in the employee selection process, please fill out the accommodations form here. If you’re unable to complete the form, you can reach out to our HR People Connect People Contact Center for support at peopleconnectvendorsnam.func@siemens\-healthineers.com. Please note HR People Connect People Contact Center will not have visibility of your application or interview status.
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Export Control: “A successful candidate must be able to work with controlled technology in accordance with US export control law.” “It is Siemens Healthineers’ policy to comply fully and completely with all United States export control laws and regulations, including those implemented by the Department of Commerce through the Export Administration Regulations (EAR), by the Department of State through the International Traffic in Arms Regulations (ITAR), and by the Treasury Department through the Office of Foreign Assets Control (OFAC) sanctions regulations.”
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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 Siemens Healthineers, 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 ($111K) sits 23% below the category median. Disclosed range: $93K to $128K.
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.
Siemens Healthineers AI Hiring
Siemens Healthineers has 1 open AI role right now. They're hiring across LLM Engineer. Based in Atlanta, GA, US. Compensation range: $128K - $128K.
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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