Staff AI Engineer

Pendo Pendo · Enterprise · New York, NY · Engineering - Emerging

Staff AI Engineer role focused on designing and building production-grade AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, for a new emergent AI products team. Responsibilities include model strategy, evaluation, guardrails, MLOps ownership, full-stack delivery, product partnership, and technical leadership. Requires deep hands-on experience with LLM systems, setting technical direction, owning outcomes, model evaluation, modern architectures, production MLOps, full-stack fundamentals, and communication skills.

What you'd actually do

  1. Design and build AI-native systems, including RAG pipelines, agentic workflows, and LLM-powered product features. You will take ideas from prototype through production and ensure they can support real users.
  2. Make principled decisions about when to prompt, when to fine-tune, and when to use a different technical approach entirely. You will explain those tradeoffs clearly to engineers and non-engineers.
  3. Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying hallucinations early. You will implement guardrails that hold up under real-world usage and load.
  4. Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will ensure AI systems are observable, performant, and production-ready.
  5. Contribute across the stack when needed to get complete AI products in front of users. This team ships products, not just models, and you will help close the gap between technical capability and user experience.

Skills

Required

  • LLM-powered systems
  • retrieval-augmented generation
  • tool use
  • agent orchestration frameworks
  • technical direction for AI systems
  • architectural patterns
  • foundational model strategy
  • AI engineering quality
  • owning outcomes across team boundaries
  • model evaluation
  • hallucinations detection and mitigation
  • modern model architectures
  • transformers
  • diffusion models
  • Production MLOps
  • model deployment
  • monitoring pipelines
  • latency optimization
  • cost management
  • reliability optimization
  • full-stack fundamentals
  • backend systems
  • frontend systems
  • communication skills
  • product judgment

Nice to have

  • fine-tuning foundation models
  • AI safety considerations
  • guardrail frameworks
  • responsible deployment practices
  • agentic or multi-step reasoning systems
  • LangChain
  • LlamaIndex
  • SaaS environment
  • product analytics environment
  • building net-new team or product area

What the JD emphasized

  • Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.
  • Demonstrated ability to set technical direction for AI systems across teams, establish architectural patterns, make foundational model strategy decisions, and raise the bar for AI engineering quality.
  • Experience owning outcomes across team boundaries, including identifying capability gaps, driving alignment across engineering and product, and influencing how a broader organization approaches AI.
  • Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.
  • Production MLOps experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in live environments.
  • Strong full-stack fundamentals and comfort working across backend and frontend systems to ship complete, user-facing AI products.
  • Exceptional communication skills, with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.
  • Demonstrated product judgment and the ability to evaluate whether something should be built before determining how to build it.

Other signals

  • AI-native experiences
  • production-grade AI systems
  • LLM-powered features
  • full-stack delivery
  • technical leadership