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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 and deploy ML models for business decisions and optimize AI agentic systems like conversational tools, using Python, SQL, and libraries such as PyTorch.
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 autonomous AI agents using LangChain, LangGraph, and AutoGen, integrating RAG, knowledge graphs, and fine-tuned SLMs for edge deployment.
Senior Full Stack Engineer builds and maintains a unified analytics web app using C# (backend) and Angular (frontend), integrating PowerBI and AI-assisted workflows. Core tasks include architecting microservices, migrating legacy reporting features, and optimizing multi-tenant data access with EF Core and SQL Server.
Builds and deploys LLM-powered agentic workflows for network troubleshooting using LangGraph, MCP servers, and Kubernetes, focusing on reasoning pipelines, tool integration, and production-scale AI systems.
Leads AI/ML and generative AI projects, designing RAG pipelines, LLM-based systems, and production-ready models while owning end-to-end delivery, strategy, and cross-functional team leadership.
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