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6625 Excellence Way, Plano, TX, USA
Source: Samsung Electronics careers · View original posting
From Samsung Electronics's posting. “We” and “our” refer to the employer.
Position Summary
Headquartered in Englewood Cliffs, N.J., Samsung Electronics America, Inc. (SEA), the U.S. Sales and Marketing subsidiary, is a leader in mobile technologies, consumer electronics, home appliances, enterprise solutions and networks systems. For more than four decades, Samsung has driven innovation, economic growth and workforce opportunity across the United States—investing over $100 billion and employing more than 20,000 people nationwide.
By integrating our large portfolio of products, services and AI technology, we’re creating smarter, sustainable and more connected experiences that empower people to live better. SEA is a wholly owned subsidiary of Samsung Electronics Co., Ltd.
To learn more, visit Samsung.com. For the latest news, visit news.samsung.com/us.
The Applied AI Lead will play a key role in driving AI-driven innovation for the Business Transformation team. This role focuses on contributing to the planning and execution of AI use cases that support company-wide AI transformation. Through these projects, this role delivers tangible business results across strategic objectives, operational efficiency, and innovation. This role reports directly to the Director, Head of AI Transformation Team.
Role and Responsibilities
Build and deploy AI automation solutions using Claude Code, Cowork, or other AI platforms, including agentic workflows, multi-step task execution, and MCP (Model Context Protocol) server integrations across development and knowledge work use cases.
Write, test, and maintain Python code across AI/ML pipelines, LLM-powered applications, data processing scripts, and internal tooling.
Work directly with business teams to uncover AI use case opportunities, join discovery workshops and weekly AI Crew meetings, and help turn operational problems into working technical solutions.
Own assigned use cases through the full project lifecycle, from early discovery and proof-of-concept through pilot and production rollout, covering technical scoping, prompt engineering, data readiness, testing, and deployment.
Track AI solution performance against defined KPIs, conduct ROI analysis, and iterate on models, prompts, and workflows based on results.
Surface technical risks and blockers across active AI projects and contribute to design decisions around model selection, tool integration, and system scalability.
Keep up with developments in AI/ML tooling, LLM frameworks, agentic AI, and RAG approaches, and share relevant findings with the team.
Skills and Qualifications
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