Note: The job is a remote job and is open to candidates in USA. Pager Health redefines how members navigate to care and wellness, validated by clinicians and powered by AI. They are seeking an Applied AI Engineer to design, build, and ship LLM-powered agents and applications, working closely with the Data Science team to create reliable, production-grade systems that address real business problems.

Responsibilities

• Build agentic applications and workflows using the LLM frameworks, models, and guardrails provided by the Data Science team Design and implement tool integrations, function-calling patterns, and orchestration logic that allow agents to take actions across internal systems and external APIs
• Translate agent specifications and prompt strategies (authored by Data Science) 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, including testing, monitoring, logging, and iteration
• Build evaluation and observability infrastructure so the team can measure agent quality, latency, cost, and safety in production
• Collaborate with Data Science on prompt engineering, model behavior tuning, and guardrail enforcement. Implementing their specifications into the runtime layer Work with platform and infrastructure teams to deploy, scale, and maintain agent services in cloud environments (GCP/Vertex AI, AWS Bedrock, Azure OpenAI)
• Contribute to internal tooling, SDKs, and shared libraries that accelerate agent development across the organization

Skills

• 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 - you don't need to pick the model, but you need to know how to get the most out of it
• Experience with API design, microservices, and deploying services in cloud environments
• Strong debugging and problem-solving instincts. Agent systems fail in non-obvious ways and you enjoy chasing down why
• Strong communication skills; ability to work across Data Science, Product, and Engineering teams
• Experience with evaluation frameworks for LLM-based systems (custom evals, RAGAS, LangSmith, Braintrust), is nice to have
• Familiarity with MLOps tooling and CI/CD for ML systems and experience with streaming responses, async architectures, and real-time agent interactions, are nice to have
• Background in building multi-agent systems with routing, delegation, and coordination patterns, is preferred
• Exposure to Google Cloud Platform/Vertex AI ecosystem
• Contributions to open-source AI/ML projects, is preferred

Benefits

• Stock options
• A range of medical, dental, vision, financial, generous PTO, stipends for professional development, and wellness benefits

Company Overview

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