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Applied AI & Machine Learning Engineers (Agents, LLMs, Production AI)
NY, USA
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Position: Applied AI & Machine Learning Engineers (Agents, LLMs, Production AI)

Location: New York / U.S. (some hybrid or onsite roles)

Compensation: ~$200K – $1M+ total comp


Overview


These are not “prompt engineer” roles.


We’re hiring engineers building real AI systems in production—agents, LLM platforms, and intelligent systems that directly impact trading, research, and decision-making. If you’ve actually shipped AI systems (not just played with APIs), this is your lane.


What You’ll Work On


  • Designing and building AI agents with memory, tools, and workflows
  • Integrating LLMs into real production systems
  • Building evaluation, monitoring, and reliability frameworks
  • Developing RAG systems, orchestration layers, and AI infrastructure
  • Deploying models into high-stakes, real-time environments
  • Collaborating with researchers, traders, and product teams


Role Variations


This posting covers multiple directions:


  • Applied AI Engineers (end-to-end AI systems)
  • AI Agents Engineers (agentic frameworks + production systems)
  • ML Engineers / Researchers (model development + infrastructure)
  • AI Product Engineers / Technical PMs (AI + product hybrid roles)


What We’re Looking For


  • 5+ years in software engineering + hands-on AI experience
  • Strong Python skills in production environments
  • Experience deploying LLMs / ML models into real systems
  • Understanding of system reliability, latency, and scaling
  • Ability to move from prototype → production → optimization
  • Bonus: experience with agent frameworks, RAG, or distributed systems


Agile Staffing is a leading recruitment firm supporting healthcare organizations across North America. We specialize in delivering experienced, hard-to-find talent to hospitals and healthcare systems through strong partnerships, high-integrity service, and modern recruitment practices. Our team is passionate about connecting great people with great organizations and building long-term success for both.


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