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Senior engineer builds and deploys LLM-powered agents and RAG systems for clients, owning presales scoping through production deployment with Python and cloud tooling.
Build and deploy production-grade AI systems, including LLMs and RAG pipelines, using Python and cloud AI platforms.
Build and deploy LLM-powered tools for an investment firm, including research assistants and internal agents, while designing scalable AI infrastructure and governance.
Build and improve AI-powered financial research tools using LLM, RAG, vector search, and agentic workflows for a market intelligence platform.
Build and deploy enterprise-grade GenAI and ML applications for asset management workflows, integrating LLMs, RAG, and vector databases with full-stack Python/React systems in a regulated environment.
Build and optimize production-grade LLM systems, integrating commercial APIs and self-hosted models, and implementing RAG pipelines and end-to-end LLM workflows.
Build and deploy enterprise-grade GenAI and ML applications for asset management workflows, including full-stack development, RAG systems, and integration layers.
Build and deploy enterprise-grade LLM applications using RAG, AI Agents, and vector databases like Milvus/Qdrant. Develop Python-based AI workflows and APIs for real-world production use.
Build and deploy enterprise-grade AI applications using LLMs, RAG, and AI agents, optimizing vector databases and knowledge bases for real-world use.
Build and maintain LLM systems, RAG pipelines, and AI agents that power trading, research, and operations for a fintech startup.
Build and optimize LLM-based trading tools like Q&A systems and agents using Python, prompt engineering, and transformer models for a crypto derivatives exchange.
Build and deploy production-grade LLM chatbots and RAG pipelines using commercial APIs and self-hosted open-source models, optimizing for latency, cost, and reliability.
Build and deploy LLM-powered AI agents, RAG systems, and backend trading infrastructure in Python to automate research, risk, and trading workflows for a quantitative trading firm.
Build and deploy AI-powered banking apps using LLMs, RAG pipelines, and Python; automate workflows with n8n and Kubernetes.
Design and deploy AI/ML models (LLMs, GenAI, CV) and agentic systems for clients, using Python, TensorFlow/PyTorch, and cloud platforms.
Lead training, alignment, and optimization of large language models using RLHF, SFT, and quantization; build reward models, red-team models, and optimize inference pipelines in Python/C++/Rust.
Build and deploy deep-learning NLP models, fine-tune LLMs, and implement RAG and agentic AI systems while maintaining MLOps pipelines.
Build AI-powered data platform tools (React UIs, Python APIs, vector stores) that let users explore and query financial data in natural language while ensuring security and correctness in a regulated environment.
Build and deploy AI agents that automate financial workflows using Python, LLM APIs (Claude/OpenAI), and Microsoft ecosystems; focus on production-grade, secure, and scalable solutions.
Designs and deploys AI/ML models and GenAI solutions (LLMs, RAG) on Azure and Databricks, embedding intelligence into enterprise data pipelines and lakehouse architectures.
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