Gen AI Architect

We are seeking a Senior AI / Generative AI Architect with deep hands-on experience designing and architecting enterprise-grade Agentic AI, LLM, RAG, and multi-agent platforms . The ideal candidate will provide technical leadership in building scalable AI architectures using Python, LangGraph, LangChain, CrewAI, RAG, vector databases, LLM orchestration, and cloud-native technologies . The Architect will be responsible for defining AI platform architecture, designing agent workflows and memory systems, establishing enterprise integration patterns, and ensuring production-grade security, observability, evaluation, scalability, and reliability of AI solutions. Key Responsibilities Architect and lead the design of enterprise-scale Agentic AI and Generative AI platforms using Python, LangGraph, LangChain, CrewAI, and LLM technologies. Define scalable architectures for single-agent and multi-agent workflows , including orchestration, routing, state management, tool/function calling, planning, and agent collaboration. Design enterprise-grade RAG architectures covering document ingestion, chunking, embeddings, retrieval, reranking, contextualization, and response generation. Architect data, memory, and knowledge layers using PostgreSQL, SQLAlchemy/Alembic, Qdrant, pgvector, and other vector/knowledge storage technologies. Establish architectural patterns for short-term and long-term agent memory , conversation state, session management, and persistent knowledge. Design and implement highly reliable FastAPI-based AI services using Pydantic, PyTest, Docker, and modern Python engineering practices. Architect integrations with enterprise data platforms and applications including Snowflake, Salesforce, Glean, Gong , and other business systems. Design secure and scalable LLM gateway, model integration, and provider abstraction layers supporting enterprise AI workloads. Define and implement LLM evaluation frameworks covering accuracy, groundedness, relevance, hallucination, latency, cost, safety, and agent/tool execution quality. Establish AI observability and monitoring architecture , including traces, metrics, logs, token usage, latency, failures, model behavior, and agent execution. Design and enforce AI security and responsible AI controls , including prompt-injection protection, data leakage prevention, guardrails, access control, and secure tool execution. Architect production deployments of AI/LLM workloads on GCP , leveraging cloud-native services, containerization, scalability, availability, and disaster-recovery practices. Establish CI/CD and engineering standards using GitHub, GitHub Actions, Docker , automated testing, code reviews, and deployment automation. Provide technical leadership and architectural guidance to AI/ML engineers and development teams. Define architecture standards, design patterns, reference architectures, technical documentation, and engineering best practices for enterprise AI. Evaluate emerging LLM, Agentic AI, RAG, MCP, vector database, and AI orchestration technologies and recommend their adoption where appropriate. Collaborate with product, data, security, cloud, engineering, and business teams to translate business requirements into scalable AI solutions. Drive AI solutions from proof of concept through production , ensuring maintainability, scalability, security, performance, and operational readiness. Required Technical Skills 15+ years of overall software/technology experience with significant experience in architecture and technical leadership. Strong hands-on experience with Python 3.11+ and enterprise Python application development. Deep expertise in Generative AI, LLMs, Agentic AI, AI agents, and multi-agent architectures . Strong experience with: LangGraph LangChain CrewAI LLM agents and orchestration Tool/function calling Agent routing and state management Agent memory RAG architectures Strong knowledge of LLM application architecture , including model selection, prompting, context management, embeddings, retrieval, and inference patterns. Hands-on experience with PostgreSQL, SQLAlchemy, Alembic , and data persistence architectures. Experience with vector databases such as Qdrant and pgvector . Strong understanding of embeddings, chunking, semantic search, vector retrieval, hybrid search, and RAG optimization . Strong experience building production APIs using FastAPI and Pydantic v2 . Experience with PyTest , automated testing, and quality engineering for AI applications. Strong experience with Docker and containerized deployments . Production experience with GCP and cloud-native AI application deployment. Experience with Snowflake and enterprise data integration. Strong understanding of GitHub and GitHub Actions , CI/CD, branching strategies, code reviews, and automated deployments. Experience designing LLM evaluation, observability, monitoring, and AI guardrail frameworks . Strong understanding of prompt injection, jailbreaks, data leakage, secure tool calling, and AI application security . Show more Show less