{"schemaVersion":"jobsearcher.job.v1","id":"36d9b6e93faee984fa6f2df8","url":"https://jobsearcher.com/jobs/36d9b6e93faee984fa6f2df8","canonicalUrl":"https://jobsearcher.com/jobs/36d9b6e93faee984fa6f2df8","title":"GEN AI Solution Architect","description":"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.","company":"AceStack","rawCompany":"acestack","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-03T02:02:01.323Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1211.00","title":"Computer Systems Analysts","slug":"computer-systems-analysts"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"GEN AI Solution Architect","description":"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.","datePosted":"2026-06-03T02:02:01.323Z","dateModified":"2026-06-03T02:02:01.323Z","hiringOrganization":{"@type":"Organization","name":"AceStack","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"36d9b6e93faee984fa6f2df8"},"url":"https://jobsearcher.com/jobs/36d9b6e93faee984fa6f2df8"}}