Gen AI Solution Architect

Roles & 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).

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).
• Middleware integration (message queues, auth systems like R2D2).

Product & Strategy

• Proven track record of building reusable ML/API products or internal platforms.