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Design and deploy AI solutions for enterprise customers, using Python, cloud AI platforms, and agentic frameworks to solve business challenges end-to-end.
Build AI/ML systems to optimize clinical trial design using GenAI, agentic frameworks, and scientific data to cut drug development time and costs.
Drive the future of Agentic AI at Pearson as part of the 'AgentOps' team. Inspire innovation. Empower learning. Why Join AgentOps Work on cutting-edge agentic AI systems at enterprise scale Help build a…
Build and deploy LLM-powered agents and RAG systems using Python, FastAPI, and vector databases; optimize token costs and model performance for scalable AI applications.
Build and scale AI/ML systems for semiconductor manufacturing, combining classical ML with Generative AI to detect anomalies, predict maintenance, and automate test plans using AWS Bedrock and RAG pipelines.
Builds and maintains a semantic data platform that turns raw enterprise data into trusted, reusable assets for AI-driven analytics and automation using modern AI coding tools and governance frameworks.
Build Python-based generative AI and agentic systems for a large bank, including FastAPI services, retrieval-augmented generation, and multi-agent orchestration.
Build and lead AI agent systems and RAG pipelines for aerospace/defense manufacturing, integrating with digital thread and MDM under ITAR/EAR constraints.
Build and ship production-grade AI solutions using LLMs, agents, and RAG pipelines for enterprise clients, integrating with Adobe Experience Platform and other marketing tech stacks.
Lead AI-native product strategy for agentic systems that streamline clinical-trial workflows, collaborating with AI, engineering, and clinical teams to prototype and scale solutions.
Build and deploy AI systems to automate and optimize NVIDIA’s chip-design workflows, including LLM-powered validation pipelines and cross-team AI integrations.
Build the control plane for enterprise AI agents: define APIs, policy engines, and observability to govern multi-cloud agent estates across AWS, Azure, Google, and third-party runtimes.
Build and deploy AI-native applications using LLMs, RAG, and agentic workflows in Java/Spring Boot and React, integrating with enterprise systems and cloud-native platforms.
Principal AI Solution Architect designs and scales enterprise-grade generative and agentic AI systems, leading PoCs, architecture reviews, and responsible AI governance for clients across industries.
Build and deploy enterprise-grade generative and agentic AI solutions end-to-end, from RAG pipelines and LLM orchestration to production monitoring and cost control, in a regulated healthcare setting.
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Build and deploy AI-powered applications using Python, LLMs, and vector databases. Design scalable GenAI systems with cloud-native tools and enterprise integration patterns.
Build and deploy ML models to predict delivery dates, detect failures, and optimize logistics for 50M+ monthly shipments using Python, Spark, and cloud platforms.
Build and deploy RAG systems and fine-tune LLMs to generate insights about flexible workspace usage, using Python, cloud MLOps, and vector databases.
Build and deploy enterprise AI agents and automation workflows that integrate with internal systems, APIs, and data platforms using Python, LangChain/LangGraph, and cloud infrastructure.
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