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Build and deploy production-grade AI systems, including LLMs and RAG pipelines, using Python and cloud AI platforms.
Build and improve AI-powered financial research tools using LLM, RAG, vector search, and agentic workflows for a market intelligence platform.
Build and optimize full-stack web and mobile platforms alongside AI-driven features like recommendations and chat interfaces using React/Next.js, Node.js, and LLM tools.
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 optimize end-to-end data pipelines on Azure and Databricks, integrating data for analytics and AI use cases using Python, Spark, and SQL.
Build and own a large-scale Web3 big data platform that ingests on-chain transactions, trading behavior, and user profiles, then layer AI/ML tools for fraud detection, risk modeling, and natural-language data querying.
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 deploy production-grade LLM chatbots and RAG pipelines using commercial APIs and self-hosted open-source models, optimizing for latency, cost, and reliability.
Build secure APIs, dashboards, and AI-powered financial research tools for a market-intelligence platform, integrating market data and collaborating with engineers, analysts, and product teams.
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.
Builds AI-augmented developer tools using .NET, Python, and cloud services, integrating LLMs and vector databases to speed up software delivery and improve workflows.
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.
Validates datasets, AI/LLM inputs and outputs, and ETL pipelines for a healthcare-focused AI platform using SQL and Python.
Build and deploy AI-powered coding agents and agentic frameworks using Python, TypeScript, and React; own cloud deployments, CI/CD, and quality assurance for AI-generated code.
Design and implement AWS-based data and AI platforms for APAC clients, ensuring scalable, compliant solutions that drive business outcomes.
Designs AI agents and LLM-based solutions with vector databases and orchestration tools, building automation workflows and scalable ML pipelines.
Design and deploy multi-agent AI systems for logistics workflows, integrating LLMs with enterprise tools using frameworks like CrewAI and LangGraph.
Architect and build production-grade AI agent systems that reliably process enterprise data, integrating LLMs, RAG, and multi-agent workflows while ensuring consistency and auditability in regulated environments.
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