Job Summary: Our client is seeking a Databricks AI Architect/Engineer with hands-on experience in Databricks Genie and Agent Bricks to build and deploy AI/LLM-based agent solutions on the Databricks platform. Job Title: Databricks AI Architect (Genie & Agent Bricks).
em> Expertise in enterprise integration architecture as applied to AI: event-driven integration patterns for AI-augmented banking workflows, API management strategy for AI capability exposure, synchronous and asynchronous AI service composition, and the specific requirements for embedding AI into core banking systems with the latency, reliability, idempotency, and audit trail guarantees that regulated financial operations require. You can design complete, end-to-end AI solutions spanning LLM integration, AI architecture, Machine Learning Architecture, MLOPs Architecture, Cloud Architecture, Agentic harnesses, agentic workflow composition, data platform dependencies, API integration topology, and production observability.
Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI). Strong experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).
This is a high-impact role responsible for driving growth, developing differentiated offerings, and guiding strategic programs to build AI-native solutions across clinical, operational, and administrative domains. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.
We are seeking a highly experienced AI Tools & Testing Architect with deep, hands-on expertise in designing, implementing, and scaling AI-driven solutions across software engineering—particularly in testing, quality engineering, and SDLC optimization. You will act as a technical architect and AI evangelist, guiding organizations in selecting the right AI tools, defining adoption frameworks, and embedding AI responsibly into engineering workflows.
RICHARDSON, MA30+ days ago
Designing LLM/RAG systems: retrieval pipelines, chunking strategies, embeddings, reranking, prompt/response orchestration, evaluation and safety. Own MLOps/LLMOps: CI/CD for models, model registry, feature store, lineage, observability, drift and cost monitoring.