AI LLM Engineer

We are seeking an experienced AI / LLM Engineer to join ourData-Driven Customer Insights teamand lead innovative Large Language Model and AI‑driven capabilities. In this role, you will contribute to theOneCustomer product, an AI-driven initiative at Boehringer Ingelheimaimed at transforming how we engage with healthcare professionals and internal stakeholders. The Dev Ops team builds and operates scalable digital solutions that enable real-time sharing of customer insights, leveraging data and cutting-edge technologies to support strategic decision-making across

Commercial,Medical, and Analyticsfunctions.

As an AI / LLM Engineer, you will play a pivotal role in designing, configuring, developing, and operating AI components that power One Customer. You will lead LLM/Machine Learning‑related topics, ranging from prompt engineering to observability and compliance, and help shape how AI augments customer understanding across the organization. You will collaborate closely with Product Owners, Engineers and external partners to bring high‑impact, production‑ready AI functionalities to life.

As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development and delivery of our products to our patients and customers. Our global presence provides opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies´ success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, mobility, networking and work-life balance.

Our competitive compensation and benefit programs reflect Boehringer Ingelheim´s high regard for our employees.

This position offers a base salary typically between $115,000 to $222,000. This position may be eligible for a role specific variable or performance based bonus and or other compensation elements. For an overview of our benefits please .

• Collaborate with cross-functional teams to understand the requirements and objectives for each product release, and align AI capabilities with business goals
• Partner with Commercial, Medical, and Analytics stakeholders to translate business knowledge into effective system prompts and system behaviors, applying few‑shot learning, updating domain ontologies, and continuously refining models and prompts based on stakeholder feedback. Manage vector databases and knowledge graphs supporting AI and RAG workflows.
• Implement and operate observability systems, covering monitoring, logging, performance tracking, error detection, and dashboards for real‑time traceability and business/compliance visibility of AI components.
• Manage LLM model configurations and deployments, overseeing endpoint setup, version control, integration consistency across environments, and infrastructure optimization for scale, load‑balancing, and high availability across environments.
• Collaborate with MLOps, QA, Data Science, and Product teams to ensure proper configuration, robust testing and continuous improvement of LLM-based features. Engineer compliance‑aligned prompts and system rules to reduce false positives and ensure market safety. Integrate user feedback loops to continuously refine prompts, models, personas, and ontologies.

• Proven experience in Dev Ops environments building AI/ML solutions
  , including work with LLMs, RAG pipelines, or knowledge graphs.
• Demonstrable experience in AI application development using no-code/low-code platforms or configuring off-the-shelf solutions together with external partners.
• Deep understanding of LLM architecture (transformers, embeddings, RAG, multimodal models) and experience fine‑tuning LLMs (LoRA, QLoRA, custom datasets). Proven knowledge of ontology management, schema design, and prompt debugging/validation.
• Hands‑on MLOps deployment
  , model optimization techniques (quantization, compression, inference acceleration) and CI/CD experience.
• Ability to follow and apply cutting‑edge research in LLMs and model safety. Experience with observability systems, API…