Skip to main content

GEN AI Solution Architect

Posted 7 days ago

ace stackMillbrae (CA)

SENIORITY

Lead

Apply (opens in a new tab)

About the role

Gen Ai Solution Architect
Location: San Francisco, CA FTEProduct Roadmap & Modular DesignDefine 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.

Before you apply

Applying takes about a minute. These four things decide how fast it moves after that.

Your profile is current

It's what we read first. Occupations, seniority and locations matter more than a long history.

Two examples you can talk through

Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.

A number in mind

What you're on now and what would make you move. We negotiate better when we know both.

Your notice period

Employers plan around it, and it's the question that stalls offers most often.

Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.

More like this