GEN AI Solution Architect

Experience: 8 - 14 years of experienceFulltime Permanent FTETechnical Expertise: GEN AI Solution ArchitectProduct 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 casesIntegrate 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)

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 searchFamiliarity 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) Proven track record of building reusable ML/API products or internal platforms