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Build full-stack web apps and integrate AI features using modern stacks (React, Node.js, Python, Java/Spring Boot) and cloud platforms (AWS/Azure/GCP), while leveraging AI coding assistants daily.
Build and maintain full-stack enterprise apps using React, FastAPI, Python, and AWS, integrating AI/ML features and DevOps practices for a biotech company.
Build and optimize scalable data pipelines and governed lakehouse platforms using Databricks, Spark, and AWS to support analytics and AI in a regulated biotech environment.
Design and build AI-ready data pipelines and products that power retrieval-augmented AI systems, ensuring data quality, governance, and secure access for AI models.
Principal ML Engineer designs, builds, and deploys production-grade AI systems using LLMs and deep learning, integrating agent workflows across GCP, AWS, and Azure.
Design and build AI-driven applications using generative models, deep learning, and cloud/on-prem pipelines. Integrate LLMs, RAG, and vector databases to create production-ready solutions.
Staff ML Engineer at Zendesk builds and scales AI-powered search solutions (e.g., RAG bots) for customer experience platforms, optimizing retrieval, ranking, and hybrid search (vector + keyword) using LLMs, PyTorch, and cloud infrastructure.
Builds and deploys AI-powered applications by integrating LLMs, developing full-stack features (React/Next.js frontends, FastAPI/Flask backends), and optimizing RAG pipelines with vector databases for scalable, production-ready solutions.
Design and implement AI governance controls, observability, and audit patterns for Huron’s AI systems, ensuring safe, compliant, and measurable outputs across cloud platforms.
Lead the architecture and hands-on delivery of scalable GenAI applications and agentic AI platforms for finance use cases using Python, AWS, and enterprise-authored AI tools.
Build and deploy production-grade generative AI systems—LLMs, RAG, and AI agents—to automate document processing, customer support, and decision workflows in a regulated banking environment.
Build and improve AI-powered tax assistant features, RAG, generative AI, workflow automation, and fiscal insights using Python, backend services, and AI orchestration frameworks.
Build and deploy production-grade generative AI systems (LLMs, RAG, AI agents) for a large bank, focusing on NLP, document intelligence, and responsible AI in a regulated environment.
Build and deploy production-ready AI-powered apps using LLMs, full-stack dev, and cloud infrastructure; optimize performance, reliability, and security.
Build and deploy ML models for Slack’s conversational AI, ranking, and generative features, using Python, PyTorch, and Spark to drive product impact at scale.
Build and integrate AI-powered features into HCM software using Python and LLMs in AWS, under senior guidance.
Design and operate Capital Group’s enterprise AI platform, building scalable vector databases, RAG pipelines, agent frameworks, and AI Gateways to securely deploy generative AI and agentic solutions across the firm.
Designs and builds enterprise-grade GenAI solutions on AWS (Bedrock, AgentCore) with LLMs, RAG pipelines, and agentic workflows, ensuring scalability, performance, and cost efficiency.
Lead the design and integration of Generative AI features—like RAG pipelines and agent workflows—into M&T Bank’s secure systems using Java, Python, or C#.
Senior AI Software Engineer specializing in Generative AI, designing and implementing production-ready applications using LLMs, agents, RAG, and cloud architectures (AWS/Azure). Core technologies include Python, LangChain, Docker, Git, and cloud services.
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