AI LLM Engineer

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 syst