Applied AI Engineer (LLM Systems / Marketplace Automation)

PulseRise TechnologiesNew York, NY
1dOnsite

About The Position

We are looking for up to two Applied AI Engineers to build and own production-grade AI systems powering an AI-native marketplace. This is a hands-on builder role focused on real-world LLM and ML applications that directly impact core business metrics such as matching quality, throughput, and operational efficiency. You will work on end-to-end AI systems including candidate-job matching, scoring models, cross-matching logic, and workflow automation. The role requires strong product instinct combined with deep technical capability in modern AI tooling and backend development. You will collaborate closely with the founder and operations teams to transform messy real-world signals into scalable AI systems. This is an early-stage opportunity for engineers who want high ownership and direct impact in a high-growth environment.

Requirements

  • 4+ years of engineering experience with meaningful production AI/LLM deployments
  • Proven experience driving AI projects end-to-end
  • Strong proficiency with LLM APIs, embeddings, vector databases, and fine-tuning workflows
  • Solid backend or full-stack development experience
  • Experience building data pipelines and deploying AI systems into production
  • Strong product intuition and ability to evaluate AI impact through metrics
  • Clear communication and ability to operate in fast-moving environments

Nice To Haves

  • Early-stage startup or founding team experience
  • Experience at an AI-native company
  • Background in recruiting or HR tech
  • Education from top-tier technical universities
  • Experience optimizing AI systems for operational workflows

Responsibilities

  • Design, build, and deploy applied AI systems for candidate-job matching and submission scoring
  • Own AI initiatives end-to-end: problem definition, experimentation, implementation, launch, and iteration
  • Make strategic decisions on prompt engineering vs. fine-tuning and model selection
  • Integrate LLM APIs, embeddings, and vector databases into production workflows
  • Build and maintain supporting backend systems and data pipelines
  • Monitor model performance in production and iterate based on real-world behavior
  • Translate operational feedback into measurable AI improvements
  • Optimize systems for speed, quality, and measurable business impact

Benefits

  • Lunch provided daily
  • Dinner provided for late office hours (after 7 pm)
  • High-ownership role in a well-funded, fast-scaling company
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