Waters is seeking an AI / ML Engineer to help shape the next generation of intelligent digital experiences that support our customers across life sciences, chemistry, and analytical research by building and evaluating large language model (LLM) systems that power smarter product search, knowledge discovery, and scientific support.
Requirements
- Hands-on experience with Python and libraries such as LangChain, Hugging Face, RAGAS, or similar for model evaluation or data processing.
- Familiarity with retrieval and ranking systems (OpenSearch, S3, AWS Bedrock) and LLM-based frameworks.
- Exposure to synthetic data generation and LLM evaluation metrics (e.g., relevance, context completeness, factual accuracy).
- Understanding of cloud-based AI workflows using AWS Bedrock, Lambda, or ECS.
- AWS (Bedrock, Lambda, ECS, S3, OpenSearch)
- Python (LangChain, Hugging Face, RAGAS, PyTorch, TensorFlow)
- LLM Evaluation (Cohere Rerank, Claude, Amazon Nova)
Responsibilities
- Build and optimize AI/ML pipelines and evaluation systems for large language models used in product search and knowledge applications.
- Develop evaluation frameworks and dashboards to measure model accuracy, latency, and user experience improvements.
- Collaborate with data engineers and product teams to preprocess, label, and integrate large datasets into AI workflows.
- Contribute to prompt tuning, accuracy testing, and continuous improvement of LLM-driven digital experiences.
- Design, build, and deploy machine learning models using frameworks like TensorFlow, PyTorch, or Hugging Face.
- Support the creation of LLM evaluation pipelines for retrieval-augmented generation (RAG) systems, automating guardrail and accuracy testing across multiple dimensions.
- Assist in data preprocessing, retrieval optimization, and performance tuning using OpenSearch, S3, and AWS Bedrock.
Other
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field with focus on AI/ML development.
- Strong analytical and problem-solving skills with the ability to learn quickly and apply feedback in iterative development cycles.
- Comfortable working with cross-functional teams and presenting project outcomes to non-technical audiences.
- Ensure adherence to AI governance, ethical AI, and data compliance standards (PCI, GDPR, CCPA).
- Stay up to date on advancements in AI/ML tools, frameworks, and evaluation methods; share insights and drive adoption across teams.
