onsite
AI Engineer - LifeScience Logistics
AI Engineer
AI Engineer focused on building and deploying large‑language‑model powered solutions, designing prompt libraries, RAG pipelines, and AI‑driven workflow optimizations using Python and AWS services.
About the role
Key Responsibilities
- Establish and maintain a structured prompt library covering summarization, Q&A, extraction, code generation, and file analysis.
- Apply advanced prompting techniques such as chain‑of‑thought, few‑shot examples, role specification, and XML‑structured inputs.
- Design and build Retrieval‑Augmented Generation (RAG) pipelines that integrate WMS, EDI logs, SOP repositories, contract data, and other enterprise systems.
- Deploy and tune LLM‑powered applications, including internal knowledge assistants, client‑facing chatbots, and RAG‑based response repositories.
- Leverage AI to optimize workflows, processes, and drive system improvements across the organization.
Requirements
- Proficiency in Python and experience building production‑grade AI services.
- Strong background in prompt engineering and large‑language‑model deployment.
- Hands‑on experience with RAG architectures and vector store integration.
- Familiarity with AWS services (SageMaker, Lambda, S3, Bedrock) for model hosting and data pipelines.
- Excellent problem‑solving skills and ability to translate business needs into technical solutions.