Gen AI Solutions Product Architect -SVP

Citi-posted 4 months ago
$156,160 - $234,240/Yr
Full-time
Irving, TX
5,001-10,000 employees
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As the Gen AI Solutions Product Architect, you will drive the development of a modular, reusable Gen AI product suite that enables cross-functional teams to deploy AI solutions rapidly without deep business context. You will architect 'plug-and-play' Gen AI modules (e.g., RAG, prompt engineering, text-to-SQL) and foster an open-source-like community for contributions. This role demands a blend of product strategy, technical architecture, and cross-functional collaboration to ensure solutions are scalable, user-friendly, and aligned with enterprise needs.

  • 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').
  • 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).
  • Partner with business teams to map their needs to pre-built modules (e.g., 'Your compliance use case fits our RAG module with these parameters').
  • Build developer tools (SDKs, UI templates) to help teams self-serve (e.g., drag-and-drop prompt builders, vector DB configurators).
  • Foster an open-source-like community: Create contribution guidelines, review external code, and incentivize modular feature additions.
  • 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.
  • 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).
  • 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).
  • Frontend integration (React/Streamlit for config UIs) and middleware (message queues, auth systems like R2D2).
  • Proven track record of building reusable ML/API products or internal platforms.
  • Ability to translate business problems into technical requirements (e.g., 'Compliance needs a RAG module with PII redaction').
  • Agile/Scrum methodology and tools (Jira, GitHub Issues).
  • Strong communication to bridge technical and non-technical stakeholders.
  • Community-building skills to drive adoption and contributions.
  • Pragmatic problem-solving (e.g., balancing customization vs. standardization).
  • Experience with open-source projects (contributor/maintainer).
  • Knowledge of LLMOps tools (PromptLayer, Weights & Biases).
  • Background in enterprise integration (SSO, RBAC, logging/monitoring).
  • Medical, dental & vision coverage.
  • 401(k).
  • Life, accident, and disability insurance.
  • Wellness programs.
  • Paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays.
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