Job Title: GEN AI Solution Architect
Location: San Francisco, CA
FTE
Job Description
•Product Roadmap & Modular Design - Define the product vision and roadmap for reusable Gen AI modules (e.g., RAG, prompting frameworks, hybrid ML/LLM systems). - Architect parameterized, business-agnostic solutions that abstract complexity (e.g., pre-configured prompts, vector DB connectors, chunking logic)
•Design APIs and microservices to expose modules as reusable components (e.g., "text-to-SQL service," "RAG-as-a-service").
•Technical Leadership - Standardize patterns (e.g., prompt templates, chunking strategies, few-shot training pipelines) across use cases
•Integrate LLM workflows (e.g., OpenAI, Claude) with traditional ML (clustering, classification) and enterprise systems (databases, UI tools). - Optimize performance of Gen AI components (cost, latency, accuracy) and ensure scalability (e.g., load balancing for vector DBs).
•Adoption & Enablement - Develop documentation, tutorials, and sandbox environments for testing modules. – Train teams on best practices (e.g., prompt engineering, security for LLM outputs)
•Track metrics: Module reuse rate, contribution volume, time-to-deploy for new use cases. Required Skills & Experience Technical Expertise - Gen AI/ML Engineering: - Hands-on experience with LLM integration (e.g., OpenAI, Anthropic, Llama 2) and frameworks (LangChain, LlamaIndex). - Expertise in RAG workflows: Document chunking (sentence transformers), vector DBs (Pinecone, FAISS), and hybrid search
•Familiarity with text-to-SQL systems, few-shot/chain-of-thought prompting, and traditional ML(clustering with scikit-learn, PyTorch). - Software Engineering: - Proficiency in Python, API design (FastAPI, Flask), and cloud platforms (AWS Sagemaker, Azure AI). - Experience with CI/CD, containerization (Docker), and infrastructure-as-code (Terraform).
•UI/Integration Skills: - Frontend integration (React/Streamlit for config UIs) and middleware (message queues, auth systems like R2D2). Product & Strategy - Proven track record of building reusable ML/API products or internal platforms.