AI LLM Engineer

Siemens Healthineers

1d•$93,680 - $128,810•Hybrid

About The Position

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

Requirements

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

Nice To Haves

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

Responsibilities

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