GEN AI Architect

Title- GEN AI Architect Location- Plano TX, 3 DAYS/ WEEK, Hybrid, Onsite Required Skills: • Deep knowledge of multi-agent architectures including planners, executors, and tool routing. • Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies. • Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation. • Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment. • Experience with observability tools (Datadog, logging, tracing, metrics). • Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning. • Hands on experience on AI • AI Engineer who has extensive knowledge on LLM creation, AI tool adoption and also defining the frameworks • should be strongly vocal enough to communicate with the customers • Design, develop, and maintain LLM-powered multi-agent workflows for code analysis, remediation proposals, and safe patch generation. • Implement agentic patterns including planning/execution loops, dynamic tool orchestration, sandboxing, guardrails, and failure recovery. • Build scalable automation systems for technical debt remediation: language/runtime upgrades, vulnerability patching, dependency modernization, and config drift correction. • Partner with Dev Experience and Platform teams to define engineering guidelines and reusable standards across the organization. • Architect and optimize Retrieval-Augmented Generation (RAG) pipelines, managing chunking, embeddings, hybrid search, reranking, and retrieval policies. • Develop robust evaluation frameworks for LLMs, RAG, and agent workflows, including offline datasets, validation metrics, statistical testing, and A/B tests. • Contribute to backend systems using Python, distributed systems, microservices, PostgreSQL, DBT, vector databases, caching, streaming, and queueing. • Build CI/CD pipelines, observability dashboards, and perform performance analysis on model, retrieval, and network layers. • Collaborate cross-functionally with product, platform, and security to move prototypes to production-grade services. • Communicate clearly with stakeholders, write technical documentation, and mentor junior engineers.