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About the role
Required Qualifications: 3+ years in customer-facing AI/ML or infrastructure roles (Field Engineer, Applied AI Engineer, Solutions Architect, ML Engineer, or similar) with a track record of owning technical workstreams in enterprise accounts. Shipped real AI/ML production code into customer environments — not just slide decks or advisory engagements. Hands-on experience with LLM inference and/or training using open-model frameworks (for example, modern serving stacks and fine-tuning workflows such as SFT; exposure to more advanced approaches like DPO or RFT is a strong plus). Strong Python, plus comfort with GPUs and cloud infrastructure (AWS, Azure, or GCP) and container/orchestration tools such as Kubernetes. Demonstrated executive-level presence: you can dive deep with an engineer and explain trade-offs to senior leadership in the same day.
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