Overview

Zimmer Biomet runs on text.

Responsibilities

• Design and build retrieval augmented generation pipelines, including chunking strategy, embedding selection, vector storage, retrieval tuning, and reranking.
• Develop and maintain prompt and context engineering for production use cases, treating prompts as versioned, tested artefacts rather than ad hoc text.
• Build agent and tool-calling workflows where a single model call is insufficient, including task decomposition, tool integration, and failure handling.
• Build evaluation harnesses for generative systems, covering groundedness, factual accuracy, hallucination rate, refusal behaviour, latency, and cost per request.
• Work with Azure OpenAI and other approved model providers, selecting models appropriate to each task and balancing quality against cost and latency.
• Build extraction pipelines that turn long, unstructured, and inconsistently formatted documents into reliable structured data.
• Implement named entity recognition, classification, summarisation, and relation extraction against domain-specific text such as clinical, regulatory, quality, and commercial content.
• Handle real-world document conditions including scanned PDFs, OCR output, tables, multi-column layouts, and inconsistent templates.
• Define and measure extraction quality with precision, recall, and error analysis on a held-out set, and drive quality up through targeted iteration rather than guesswork.
• Build human-in-the-loop review steps where the confidence threshold or the regulatory context requires them.
• Select, fine-tune, and evaluate language models and embedding models against clearly defined success criteria for each use case.
• Build and maintain semantic search and similarity capability, including embedding strategy and index management.
• Create and curate labelled datasets, including annotation guidelines and inter-annotator agreement where the task warrants it.
• Establish baselines before reaching for complexity, and demonstrate that added complexity earns its place.
• Conduct error analysis on production systems and feed findings back into the pipeline, the prompt, the model choice, or the data.
• Write production-quality Python: tested, reviewed, documented, and maintainable by someone other than you.
• Deploy NLP services on Azure and support them in production, working with platform engineers on packaging, CI/CD, and observability.
• Monitor deployed systems for quality drift, cost, and latency, and act on what the monitoring shows.
• Handle sensitive data correctly, applying Zimmer Biomet policies for data privacy, information security, and responsible AI, and flagging use cases that may carry regulatory or validation obligations.
• Document design decisions, evaluation results, and known limitations so that the work can be reviewed, audited, and built on.
• Collaborate with business stakeholders to frame problems, agree success criteria, and manage expectations on what is and is not achievable.

Qualifications

• Strong Python, including the scientific and NLP ecosystem such as PyTorch or TensorFlow, Hugging Face Transformers, spaCy, scikit-learn, and pandas.
• Practical command of LLM application frameworks and patterns, for example LangChain, LlamaIndex, or equivalent.
• Solid understanding of transformer architectures, tokenisation, embeddings, fine-tuning approaches, and their practical trade-offs.
• SQL and comfort working with data at scale.
• Software engineering fundamentals: version control with Git, automated testing, code review, and CI/CD.
• Cloud experience, ideally Microsoft Azure, including deploying and running a service rather than only notebook-based development.
• Rigorous about measurement, and willing to report a negative result rather than a flattering demo.
• Pragmatic about complexity, favouring the simplest approach that meets the requirement.
• Excellent written and verbal English communication, with the ability to explain model behaviour and its limits to a non-technical audience.
• Able to work independently across time zones with distributed stakeholders and to manage several use cases in parallel.
• Familiarity with responsible AI practice, including bias evaluation, model cards, and AI governance frameworks.
• Contributions to open source NLP tooling, publications, or comparable public technical work.
• Your Background
• Working Relationships
• This role reports to the Director of AI within the Enterprise AI function. It works closely with AI platform and data engineers on infrastructure and deployment, with information security and privacy teams on data handling, with quality and regulatory colleagues on validation expectations, and directly with business stakeholders across all regions who own the use cases.
• Physical Requirements
• Travel Expectations
• EOE/M/F/Vet/Disability

Education

• Bachelor’s or Master’s degree in computer science, computational linguistics, data science, engineering, mathematics, or a related field, or equivalent practical experience.
• Three to five years building natural language processing or machine learning systems, including at least one that reached production and real users.
• Demonstrable experience building applications on large language models, including retrieval augmented generation and prompt engineering for production workloads.