AI LLM Engineer

Duties & Responsibilities

• Lead all LLM-related activities, including prompt engineering, compliance-rule prompts, user-feedback integration, parameter tuning, data-structure understanding, model/API version upgrades.
• 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.

Requirements

• Proven experience in DevOps 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 operations, and scalable cloud architectures.
• Ability to work collaboratively in cross-functional product teams and to communicate technical topics clearly to non-technical stakeholders.
• Strong analytical, problem-solving, and cross-functional collaboration skills.

Eligibility Requirements

• Must be legally authorized to work in the United States without restriction.
• Must be willing to take a drug test and post-offer physical (if required).
• Must be 18 years of age or older.

Desired Skills, Experience and Abilities

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Engineering, or a related field with a strong emphasis on AI and machine learning technologies.