Applied AI Engineer

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
• Contribute to internal tooling, SDKs, and shared libraries that accelerate agent development across the organization

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 - 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
• You will be encouraged to shape your job, stretch your skills and drive the company's future. Y

Benefits

Additional Information

Pager Health℠ redefines how members navigate to care and wellness, validated by clinicians and powered by AI. Our solutions help people get the right care at the right time in the right place and stay healthy, while simultaneously reducing system friction and fragmentation, powering engagement, and orchestrating the enterprise. Pager Health partners with leading payers, providers and employers representing more than 26 million individuals across the United States and Latin America. We're looking for an Applied AI/LLM Engineer to design, build, and ship LLM-powered agents and applications. You'll work closely with our Data Science team, who defines the LLM strategy, guardrails, evaluation criteria, and model selection. Your job is to take that foundation and turn it into reliable, production-grade agent systems that solve real business problems. This is a builder role, not a research role. You'll spend most of your time writing code - wiring up tools, orchestrating multi-step workflows, integrating APIs, and making sure agents behave predictably in the wild. You should be deeply comfortable working with large language models in production and operating at the intersection of software engineering and applied AI.

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