Citigroup Inc.
Role Overview
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. Key Responsibilities
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).
Cross-Functional Collaboration
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.
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.
Technical Expertise
Gen AI/ML Engineering: Hands-on experience with LLM integration (e.g., OpenAI, Anthropic, Llama 2) and frameworks (LangChain, LlamaIndex). RAG workflows: Document chunking (sentence transformers), vector DBs (Pinecone, FAISS), and hybrid search. 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). 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). Product & Strategy
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). Soft Skills
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). Preferred Qualifications
Experience with open-source projects (contributor/maintainer). Knowledge of LLMOps tools (PromptLayer, Weights & Biases). Background in enterprise integration (SSO, RBAC, logging/monitoring). Education
Bachelor's/University degree in Computer Science, Data Engineering, Cloud Computing, Technology or a related field, or equivalent experience. Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Citi's Accessibility at Citi, the EEO Policy Statement and the Know Your Rights poster.
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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. Key Responsibilities
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).
Cross-Functional Collaboration
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.
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.
Technical Expertise
Gen AI/ML Engineering: Hands-on experience with LLM integration (e.g., OpenAI, Anthropic, Llama 2) and frameworks (LangChain, LlamaIndex). RAG workflows: Document chunking (sentence transformers), vector DBs (Pinecone, FAISS), and hybrid search. 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). 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). Product & Strategy
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). Soft Skills
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). Preferred Qualifications
Experience with open-source projects (contributor/maintainer). Knowledge of LLMOps tools (PromptLayer, Weights & Biases). Background in enterprise integration (SSO, RBAC, logging/monitoring). Education
Bachelor's/University degree in Computer Science, Data Engineering, Cloud Computing, Technology or a related field, or equivalent experience. Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Citi's Accessibility at Citi, the EEO Policy Statement and the Know Your Rights poster.
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