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Pager Health

Powering connected healthcare

Applied AI Engineer

AI EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 201-500Since 2014H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

81 days ago

Salary

$150K - $160K / year

Seniority

Senior

Bachelor Degree3 yrs expEnglishMicroservicesPython

Job Description

Applied AI Engineer

Pager Health

• Build agentic applications and workflows using the LLM frameworks • Design and implement tool integrations, function-calling patterns, and orchestration logic • Translate agent specifications and prompt strategies into robust, deployable services • Implement RAG pipelines, including vector store integration, chunking strategies, and retrieval optimization • Own the full lifecycle of agent systems from prototype through production • Build evaluation and observability infrastructure • Collaborate with Data Science on prompt engineering, model behavior tuning, and guardrail enforcement • Work with platform and infrastructure teams to deploy and maintain agent services • Contribute to internal tooling, SDKs, and shared libraries

Job Requirements

  • 3+ years of software engineering experience with strong proficiency in Python
  • Hands-on experience building applications powered by large language models (Claude, GPT, Gemini)
  • Familiarity with agent frameworks and orchestration patterns (LangChain, LangGraph, CrewAI, Vertex AI Agent Builder, or custom orchestration)
  • Experience implementing function calling, tool use, and multi-step agent workflows
  • Solid understanding of RAG architectures, embedding models, and vector databases (Pinecone, Weaviate, pgvector, Vertex AI Vector Search)
  • Comfort working within defined guardrails and model configurations
  • Experience with API design, microservices, and deploying services in cloud environments
  • Strong debugging and problem-solving instincts
  • Strong communication skills; ability to work across Data Science, Product, and Engineering teams
  • Experience with evaluation frameworks for LLM-based systems is nice to have
  • Familiarity with MLOps tooling and CI/CD for ML systems

Benefits

  • stock options
  • medical, dental, vision benefits
  • financial benefits
  • generous PTO
  • stipends for professional development
  • wellness benefits

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