LLM Products)

W3 Global Sourcing•San Francisco, CA

About The Position

We're representing our prestigious client who is building a next-generation, AI-native platform transforming how people access and experience healthcare. This is an opportunity to join a foundational team shaping intelligent systems that directly impact real users at scale. We're looking for a product-minded AI/ML engineer who thrives in ambiguity, ships fast, and builds systems that matter.

Requirements

8+ years of professional experience in software engineering or ML engineering
5+ years building ML systems in production environments
Proven experience building and shipping LLM or RAG-based systems in production
Strong proficiency in Python and backend development
Experience designing and deploying end-to-end ML systems, not just modelling
AI / ML Expertise
Hands-on experience with: LLMs (OpenAI, open-source models, etc.)
Retrieval-Augmented Generation (RAG)
Agent frameworks and orchestration tools
NLP / information retrieval / semantic search
Document processing, summarization, or knowledge systems evaluation frameworks
prompt iteration
retrieval optimization
Engineering Strength
Strong software engineering fundamentals: APIs, distributed systems, and backend architecture
Comfortable owning systems end-to-end in production

Nice To Haves

You've built AI products used by real users, not just internal tools
You've taken systems from idea production iteration
You think in terms of user impact, not just model performance
You're excited by fast-moving, ambiguous environments and take ownership without waiting for direction
Experience in healthcare or regulated environments is a plus, but not required

Responsibilities

Design, build, and deploy end-to-end AI-powered products using modern LLMs and orchestration frameworks
Own and ship 01 features such as: Conversational AI assistants, Intelligent search and retrieval systems, Personalized recommendations
Build scalable backend systems and APIs powering AI-driven user experiences
Work across the stack — from data ingestion and modeling to real-time inference and product integration
Partner closely with product, design, and clinical stakeholders to translate complex data into intuitive, high-impact tools
Drive technical direction and architecture decisions across AI systems

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