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Build and maintain cloud-native .NET Core and React/Angular apps for fintech compliance and payments systems, integrating AI tools and DevSecOps practices.
Build and own AI agent infrastructure and automation systems that connect LLMs to internal tools and data, enabling self-service workflows for marketing and sales teams using Python, JavaScript, and GCP serverless services.
Senior AI Engineer Remote — US only · Full-time Are you passionate about building AI products people actually use, serving millions of users? Do you want to help lead the AI engineering effort at this country's…
Вакансия открыта в Инновационном центре «Безопасный транспорт», который входит в структуру Московского метрополитена. С 2017 года мы собираем и анализируем Big Data всего Транспортного комплекса Москвы. Эти данные…
Own the architecture of an AI-native banking platform, reviewing AI-generated code, defining system boundaries, and guiding Python/FastAPI backend and React/TypeScript integration patterns.
Senior engineer building AI features end-to-end—from prototype to production—across full stack, AI layers, and healthcare domain, using agentic tools like Claude Code and modern frameworks.
Build and deploy production-grade AI systems using RAG, agentic frameworks (LangGraph, AutoGen), and vector search (Azure AI Search, pgvector) with Python and cloud tools.
Principal Engineer to design and lead Bullish’s AI & Data Platform, building semantic layers, knowledge graphs, and conversational analytics for institutional crypto and fintech use cases.
Lead the design and deployment of agentic AI systems for Citi’s banking operations, using Python, Google ADK, LangChain, and LLMs to automate workflows and reduce risk.
Build production-ready GenAI systems using Python, React, and AWS, including LLM pipelines, RAG, and agent-based architectures for scalable AI products.
Principal AI/ML Architect designs and advises on production ML systems, MLOps/LLMOps pipelines, and GenAI architectures on AWS for enterprise clients, translating technical depth into business value.
Build and lead Gen AI products using Python, React, and AWS, designing LLM pipelines, Agentic AI, and RAG systems while collaborating with cross-functional teams.
Leads the architecture and development of an AI system that dynamically assembles personalized scenarios from modular AI components, using Python, LangChain, and RAG pipelines.
Build and deploy enterprise-grade AI agents and multi-agent systems using Python, LangChain/LangGraph, and RAG architectures for scalable copilots and automation.
Builds production-grade AI applications (chatbots, RAG, agents) by refactoring prototypes into scalable, secure, and observable solutions using Azure AI services, full-stack tech (React/TypeScript, Python, .NET), and responsible AI patterns. Bridges business needs with engineering teams to accelerate AI adoption while ensuring governance, compliance, and maintainability.
Build full-stack AI applications that integrate generative AI, RAG, and agentic workflows using .NET, Python, Blazor, and React, delivering secure, scalable enterprise solutions.
Build Python-based generative AI and agentic systems for a large bank, including FastAPI services, retrieval-augmented generation, and multi-agent orchestration.
Build and scale production-grade RAG systems for enterprise search and question answering using LLMs, vector databases, and retrieval pipelines.
Build and deploy agentic AI systems for financial data, focusing on LLM orchestration, retrieval, and scalable workflows to power research and insights.
Build and scale production-grade RAG pipelines, retrieval systems, and LLM orchestration for enterprise search and knowledge discovery in financial data.
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