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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.
Design and build AI-powered applications using cloud services, generative models, and deep learning. Integrate solutions like chatbots and image processing into production pipelines.
Lead the security architecture for JPMorganChase’s AI/ML platforms, designing controls against threats like prompt injection and data poisoning while guiding secure AI agent development and deployment.
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.
Build, train, and deploy ML models using Python and frameworks like TensorFlow/PyTorch to power AI-driven business solutions.
Define and lead the enterprise architecture for Nuvei’s adoption of AI agents, establishing lifecycle standards, secure platforms, and governance to deploy agentic systems at scale in fintech.
Designs scalable AI-enabled workflow platforms and agentic automation for Daimler Truck’s engineering environments, integrating model serving, enterprise systems, and CI/CD pipelines.
Lead AI/ML architecture and development for fintech products, building and deploying production-grade generative AI systems including LLMs and RAG pipelines.
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.
Designs enterprise-scale cloud and AI solutions for a financial services firm, guiding teams from concept to delivery while balancing business needs and technical constraints.
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, document, and optimize production-grade AI/ML pipelines and model integration layers using Python, PyTorch, and vector databases.
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 evaluate enterprise AI systems, including LLMs, voice agents, and multimodal models, using prompt engineering, fine-tuning, and reinforcement learning to improve reliability and customer impact.
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.
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