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Design and operate PostgreSQL clusters and vector search systems to power Kraken’s crypto exchange and AI features, focusing on performance, reliability, and automation.
Builds backend services and APIs in Python/TypeScript that orchestrate LLMs, vector databases, and agentic workflows for an AI-driven infrastructure automation platform.
Lead architecture and development of secure, scalable healthcare platforms integrating AI models, multi-modal databases, and real-time data pipelines for clinical insights.
Design and lead enterprise AI solutions for Australian clients, building secure LLM/RAG architectures and rapid PoCs while mentoring AI engineers.
AI Data Engineer (Data & Analytics) Data and Analytics at COMPLY: COMPLY is the worlds leading aggregator of financial and regulatory data to support compliance. Our mission is to help financial institutions meet…
Build and own AI/LLM systems for corporate spending management, including RAG, agent orchestration, and security controls, using Python and cloud-native tools.
Build and own Qashio’s AI/LLM platform, designing secure, vendor-neutral systems for agent orchestration, RAG, and compliance controls from prototype to production.
Build and deploy AI agents using LLMs (OpenAI, Claude, Gemini) and frameworks like LangChain to automate workflows and enhance fintech operations.
Build and deploy enterprise-grade AI solutions, including GenAI agents and RAG systems, using Python, cloud platforms (Azure/AWS), and frameworks like LangChain to improve customer experience and operational efficiency in an insurtech company.
Build and own AI/LLM systems for a fintech expense platform, designing secure RAG, agent orchestration, and evaluation pipelines in Python.
Build and deploy AI-powered applications using LLMs, RAG pipelines, and vector search. Integrate LLM APIs into production systems with Python and cloud tools.
Build production-grade AI agents and LLM-powered features like chatbots and RAG systems using Python, FastAPI, and third-party models (OpenAI, Gemini, Claude).
Build and scale Airweave’s distributed data pipelines, vector databases, and LLM inference infrastructure to power thousands of AI agents reliably at scale.
Build and maintain RAG pipelines: clean and transform unstructured data, generate embeddings, and optimize vector indexes for retrieval quality.
Build and maintain data pipelines that clean, normalize, and embed unstructured documents for RAG systems using Python, Ollama, and Supabase pgvector.
Build and scale the backend platform that powers AI-driven travel experiences, integrating ML models and vector search for real-time personalization at global scale.
Builds and scales data pipelines, crawlers, and semantic search systems using Golang/Node.js, MongoDB, PostgreSQL, and vector databases to power AI-driven financial analytics and RAG workflows.
Build and scale data pipelines, crawlers, and semantic search systems using Golang/Node.js, PostgreSQL, and vector databases to power AI-driven financial analytics and RAG integrations.
Build full-stack MERN apps with TypeScript, integrate RAG pipelines and LLM services, and deploy on AWS/GCP.
Full Stack Engineer building Python-based microservices with React frontends, deploying via Docker and CI/CD, and occasionally on-site at client locations in Italy.
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