• work with user groups to solve business problems with agentic AI.
• build a world class agentic AI enterprise capability leveraging cloud platforms, analytics, and agile development methodologies.
• meet with industry experts, reviewing/selecting enterprise technology tools and processes, designing solutions, and getting hands on with the implementation.

Requirements

Proven experience working with ML engineering teams and delivering ML-powered systems in production at scale.
Understanding of generative AI systems, including hands-on experience working with large language models (LLMs), embeddings, retrieval pipelines, prompt engineering, and evaluation strategies.
Strong engineering fundamentals: modern software development practices, cloud-native architectures (preferably in Azure), and ML lifecycle tools.
Experience designing AI capabilities or shared services used by multiple product teams.
Familiarity with tools like LangChain, LangGraph, LlamaIndex, OpenAI APIs, HuggingFace, Weaviate/FAISS, and vector databases.
Experience in regulated industries like healthcare, insurance, or finance.
Prior experience standing up developer platforms or internal ML tooling.
Excellent communication and collaboration skills—you can translate between technical and business stakeholders and rally others around a shared vision.

🔍 ATS Optimization Keywords
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Hard Skills

machine learning engineering
generative AI systems
large language models
embeddings
retrieval pipelines
prompt engineering
evaluation strategies
cloud-native architectures
ML lifecycle tools
AI capability design

Soft Skills

communication skills
collaboration skills
stakeholder management
vision alignment

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