Senior AI Engineer, Platform Engineering

Reposted 21 Days Ago
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Halifax, NS, CAN
In-Office
Senior level
Healthtech
The Role
Design, build, and deploy production-grade AI agents, RAG solutions, and autonomous workflows; integrate agents with enterprise platforms; optimize retrieval, observability, security, performance, and cost; create reusable connectors and best practices to drive scalable AI adoption.
Summary Generated by Built In

Global Technology Solutions (GTS) at ResMed is a division dedicated to creating innovative, scalable, and secure platforms and services for patients, providers, and people across ResMed. The primary goal of GTS is to accelerate well-being and growth by transforming the core, enabling patient, people, and partner outcomes, and building future-ready operations.

The strategy of GTS focuses on aligning goals and promoting collaboration across all organizational areas. This includes fostering shared ownership, developing flexible platforms that can easily scale to meet global demands, and implementing global standards for key processes to ensure efficiency and consistency.

About the Role:
We are seeking a Senior AI Engineer – Platform Engineering to design, build, and deploy intelligent agent-based solutions that solve business and engineering problems.
You will leverage cloud-native and AI platform capabilities to develop production-grade AI applications, autonomous workflows, and Retrieval-Augmented Generation (RAG) solutions that improve productivity, automate complex processes, and enhance decision-making across the organization.
This role requires a strong blend of software engineering, AI application development, systems integration, and solution architecture. The ideal candidate has hands-on experience building AI agents, Retrieval-Augmented Generation (RAG) solutions, and tool-driven workflows using modern LLM frameworks.
Responsibilities:
• Design, build, and deploy AI agents and agentic workflows that solve business and engineering challenges using modern LLM frameworks.
• Develop AI-powered applications leveraging Retrieval-Augmented Generation (RAG), vector databases, embeddings, tool calling, and agent orchestration patterns.
• Optimize retrieval quality and response accuracy through chunking strategies, metadata enrichment, hybrid search, reranking, evaluation, and continuous improvement.
• Integrate AI solutions with enterprise platforms such as GitHub, Jira, Confluence, Datadog, Slack, Kubernetes, and internal APIs.
• Develop reusable connectors, tools, and agent capabilities that enable agents to retrieve information and perform actions across systems.
• Establish best practices for testing, evaluation, observability, security, and governance of AI applications.
• Monitor and optimize agent performance, reliability, latency, accuracy, and cost while troubleshooting production issues and continuously improving solution quality.
Required Qualifications:
• 5+ years of experience in Software Engineering, Platform Engineering, or Cloud Engineering
• Hands-on experience building AI applications using frameworks such as LangGraph, Strands Agents, OpenAI Agents SDK, or similar agent orchestration frameworks.
• Strong programming skills in TypeScript, Go and/or Python
• Experience designing and building microservice applications
• Experience integrating APIs and enterprise systems
• Hands-on experience building production AI applications using LLMs
Strong understanding of:
• Retrieval-Augmented Generation (RAG)
• Vector databases and embeddings
• Tool calling and agent orchestration
• Structured outputs, context engineering, and agent orchestration
• AI evaluation and observability
Preferred Qualifications:
• Experience building AI copilots, digital assistants, or autonomous agents
• Experience integrating AI solutions with GitHub, Jira, Slack, Datadog, Confluence, or similar enterprise platforms
• Experience with vector databases such as OpenSearch
• Experience with AI observability and evaluation tools such as Langfuse or Datadog LLM Observability
• Experience building MCP servers and agent interoperability patterns
• Experience with Kubernetes and cloud-native platforms
What Success Looks Like:
• Deliver production-ready agentic AI solutions that solve real business and engineering challenges
• Build reusable AI workflows, tools, and integrations that accelerate adoption across teams
• Improve productivity and operational efficiency through intelligent automation
• Drive measurable outcomes through scalable, secure, and reliable AI applications

Joining us is more than saying “yes” to making the world a healthier place. It’s discovering a career that’s challenging, supportive and inspiring. Where a culture driven by excellence helps you not only meet your goals, but also create new ones. We focus on creating a diverse and inclusive culture, encouraging individual expression in the workplace and thrive on the innovative ideas this generates. If this sounds like the workplace for you, apply now! We commit to respond to every applicant.

 

Skills Required

  • 5+ years in Software Engineering, Platform Engineering, or Cloud Engineering
  • Hands-on experience with agent orchestration frameworks (e.g., LangGraph, Strands Agents, OpenAI Agents SDK)
  • Strong programming skills in TypeScript, Go and/or Python
  • Experience designing and building microservice applications
  • Experience integrating APIs and enterprise systems (GitHub, Jira, Confluence, Datadog, Slack, internal APIs)
  • Hands-on experience building production AI applications using LLMs
  • Strong understanding of Retrieval-Augmented Generation (RAG)
  • Strong understanding of vector databases and embeddings
  • Strong understanding of tool calling and agent orchestration patterns
  • Strong understanding of structured outputs, context engineering, and agent orchestration
  • Strong understanding of AI evaluation and observability
  • Experience building AI copilots, digital assistants, or autonomous agents
  • Experience with vector databases such as OpenSearch
  • Experience with AI observability and evaluation tools (e.g., Langfuse, Datadog LLM Observability)
  • Experience building MCP servers and agent interoperability patterns
  • Experience with Kubernetes and cloud-native platforms

ResMed Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about ResMed and has not been reviewed or approved by ResMed.

  • Strong & Reliable Incentives Bonuses are considered a meaningful component of total compensation and are paid regularly. Annual payouts and performance incentives are frequently highlighted alongside base pay.
  • Healthcare Strength Health coverage is described as comprehensive, including medical, dental, and vision plans that are viewed favorably. Wellbeing resources and flexibility around care reinforce the overall strength of the offering.
  • Equity Value & Accessibility An employee stock purchase plan is broadly available and regarded as a valuable ownership benefit. Equity elements are positioned as accessible parts of total rewards across many roles.

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The Company
HQ: San Diego, CA
5,300 Employees
Year Founded: 1989

What We Do

ResMed provides medical equipment for treating, diagnosing, and managing sleep-disordered breathing and other respiratory disorders.

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