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15 часов назад

Senior Applied AI Engineer (AI)

200 000 - 270 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR

Senior Applied AI Engineer (AI): Building and productionizing enterprise AI programs and agentic workflows with an accent on LLM quality, RAG, and scalable deployment. Focus on designing evaluation frameworks, mentoring engineers, and serving as a strategic technical advisor to strategic customers.

Location: Must be based in the US (Menlo Park, CA) and be willing to travel at least 25% of the time.

Salary: $200K – $270K

Company

A data cloud company powering the era of the agentic enterprise through high-impact AI innovation.

What you will do

  • Lead the full lifecycle of complex, multi-engineer AI engagements from scoping and architecture to deployment.
  • Define quality metrics and evaluation frameworks to systematically improve agent accuracy, faithfulness, and safety.
  • Provide technical leadership and mentorship to a team of 2–6 Applied AI Engineers through code and design reviews.
  • Design and ship high-quality ML pipelines and agentic AI solutions in secure, large-scale production environments.
  • Act as a senior technical advisor to customer data science and engineering leadership.
  • Collaborate with internal Product and Engineering teams to shape the future of the hirify.global AI platform.

Requirements

  • 5+ years of professional software engineering experience.
  • Proven experience building and productionizing LLM applications, specifically using RAG and agentic workflows.
  • Demonstrated experience leading technical projects and setting technical direction for a team.
  • Hands-on experience defining evaluation frameworks and quality metrics for LLM systems.
  • Experience in a customer-facing technical role with excellent communication skills.
  • Willingness to travel at least 25% of the time.

Nice to have

  • Experience with observability tooling such as Braintrust, LangSmith, Arize, Weave, or Promptfoo.
  • Experience with MLOps lifecycle, including deployment and monitoring in AWS, Azure, or GCP.
  • Familiarity with data science libraries including pandas, numpy, and Snowpark.
  • Startup experience or experience working in a high-growth, fast-paced environment.

Culture & Benefits

  • Impact-driven culture focused on innovation and collaboration.
  • Opportunity to work at the forefront of the agentic enterprise AI era.
  • Experimental mindset that encourages rapid testing of emerging capabilities.
  • Dynamic, fast-moving environment designed for building big and scaling quickly.

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