Generative AI Technical Architect

We are looking for a hands-on **Generative AI Technical Architect** who will own the end-to-end architecture of enterprise-scale, knowledge-intensive, agentic AI systems. This is a high-impact role focused on building production-grade Retrieval-Augmented Generation (RAG), Corrective/Controllable-Augmented Generation (CAG), multi-agent frameworks, long-term memory systems, NL2SQL engines, and Small Language Model (SLM)-powered edge/agent deployments using modern ecosystems (LangChain, LlamaIndex, CrewAI, AutoGen, Haystack, DSPy, etc.).

Responsibilities
 

- Architect and own the enterprise GenAI platform with advanced RAG/CAG pipelines (hybrid search, re-ranking, query rewriting, hypothetical document embeddings (HyDE), parent-child retrieval, knowledge graph + vector fusion).

- Design and scale multi-agent / agentic workflows (reasoning + acting, tool use, multi-agent collaboration, hierarchical agents, long-running agents with persistence).

- Build production-grade long-term and short-term memory systems (vector stores with metadata filtering, session summarization, entity memory, reflection/memory consolidation).

- Lead architecture of enterprise Knowledge Bases (ingestion pipelines, chunking strategies, metadata enrichment, incremental updates, multi-tenant KB isolation).

- Own NL2SQL / Text-to-SQL architecture (schema linking, few-shot prompting, self-correction, execution feedback loops, SQL guardrails, multi-database support).

- Design and deploy Small Language Models (SLM) for on-device, low-latency, or cost-sensitive agent use cases (Phi-3, Gemma-2B, Mistral-7B, Llama-3.1-8B quantized, TinyLlama, MobileBERT variants).

- Define the standard GenAI framework stack (LangChain / LlamaIndex / LangGraph / CrewAI / AutoGen / Microsoft Semantic Kernel / Haystack / DSPy) and create internal libraries/SDKS for the entire organization.

- Build observability, tracing, and evaluation frameworks for RAG (RAGAS, TruLens, DeepEval), agents (AgentOps), and NL2SQL accuracy.

- Establish governance: prompt injection defense, output sanitization, PII redaction, citation verification, hallucination detection, and enterprise guardrails.

- Performance engineering: latency optimization (speculative decoding, caching, batching, query routing), cost optimization (SLM routing, fallback strategies), and multi-region deployment.

- Drive GenAI platform roadmap, conduct architecture reviews, and mentor senior engineers building RAG/agent products.