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Build AI-powered applications using LLMs, RAG pipelines, and vector databases; implement agents, prompts, and evaluation loops while integrating enterprise APIs and data sources.
Build AI-powered applications using LLMs, RAG pipelines, and vector search; implement agents, prompts, and CI/CD while integrating enterprise APIs and data sources.
Build AI-powered applications using LLM tooling, RAG pipelines, and vector search; implement agents, prompts, and evaluation loops while integrating APIs and data sources.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases. Develop agents, prompts, and integrations with Python/TypeScript/Java, and deploy scalable prototypes.
Build AI-powered applications using LLMs, RAG pipelines, and vector search. Develop agents, prompts, and integrations with clean code and CI/CD, then prototype rapidly into production systems.
Build AI-powered applications using LLMs, RAG pipelines, and vector search; implement agents, prompts, and evaluation loops while integrating enterprise APIs and data sources.
Build enterprise-grade GenAI apps in Python: LLMs, RAG pipelines, vector search, and agentic workflows for scalable, AI-powered solutions.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating enterprise APIs and cloud services while delivering rapid prototypes.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating enterprise APIs and deploying scalable services with Java/Python/TypeScript.
Builds AI-powered agentic applications by implementing LLM tooling, RAG pipelines, and vector search, integrating with enterprise systems, and ensuring safety/testing. Core tech: Python, TypeScript/Node.js, Java, LangChain, vector DBs (pgvector, Pinecone).
Build AI-powered applications using Java full-stack, Python, and TypeScript, implementing agents, RAG pipelines, and vector search for rapid prototyping and production systems.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating enterprise APIs and ensuring robust testing and safety.
Build AI-powered agentic applications using LLMs, RAG pipelines, and vector databases, integrating APIs and cloud services while writing clean, testable code and CI/CD pipelines.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases; implement agents, prompts, and APIs while owning full-stack development and CI/CD.
Build AI-powered applications using LLMs, RAG pipelines, and vector databases; implement agents, prompts, and CI/CD while integrating enterprise APIs and ensuring safety.
Design and build a multi-agent GenAI reasoning layer using Amazon Bedrock, RAG, and AWS services to power a voice/chat HUD FHA Resource Center with grounded, safe interactions.
Build full-stack features and internal AI tools for a freight-tech startup, using TypeScript/React and LLM APIs to ship production code and agentic workflows.
Own AI-focused data platform features from PRFAQ to launch, partnering with engineering and GTM to ship vector search, RAG, and agent frameworks for enterprise customers.
Build and deploy AI-enabled applications and agentic workflows for clients, designing the systems around models, tools, and safeguards to ensure reliability in production.
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