Machine Learning Ops Engineer

- Platform observability experience: Prometheus, Loki, Grafana and/or Datadog — metrics, logs, dashboards, alerting, SLOs. - Strong production Python (and SQL for data work). - Experience building CI/CD pipelines and APIs end-to-end — GitHub / GitHub Actions, CodePipeline or Jenkins.

About the role ResMed’s AI platform powers dozens of data scientists and a growing set of GenAI / Agentic AI products that touch patients, clinicians, and providers worldwide. We run on AWS and Kubernetes, provisioned with Terraform, and shipped through modern CI/CD. We are looking for AI/ML Platform Engineer whose core is Kubernetes, AWS, Terraform, AI and platform observability — someone who can design, build, and operate the platform end-to-end and instrument it so nothing is a mystery in production. You should also bring an AI working mindset: curious about how ML and agentic workloads run on the platform, comfortable partnering with data scientists and GenAI teams, and eager to grow the platform toward LLMOps and Agentic AI as those workloads scale.

ResMed’s AI platform powers dozens of data scientists and a growing set of GenAI / Agentic AI products that touch patients, clinicians, and providers worldwide. We run on AWS and Kubernetes, provisioned with Terraform, and shipped through modern CI/CD. We are looking for AI/ML Platform Engineer whose core is Kubernetes, AWS, Terraform, AI and platform observability — someone who can design, build, and operate the platform end-to-end and instrument it so nothing is a mystery in production. You should also bring an AI working mindset: curious about how ML and agentic workloads run on the platform, comfortable partnering with data scientists and GenAI teams, and eager to grow the platform toward LLMOps and Agentic AI as those workloads scale. What you’ll do

What you’ll do - Design, build, and operate the AI/ML platform on AWS + Kubernetes — clusters, networking, IAM, storage, cost, and reliability. - Provision and evolve infrastructure with Terraform; treat infra as code with real review and rollback.

- Deep, hands-on Kubernetes in production. - Hands-on AWS — comfortable with 3+ of: EKS, Lambda, EC2, S3, IAM, Networking (VPC, ALB/NLB), RDS, EMR, Glue, Athena, Batch, SageMaker, MWAA/Airflow. - Working command of Terraform — modules, state, reviews, drift.

- Running AI agents on Kubernetes: containerizing agent workloads, autoscaling (HPA/KEDA), stateful sessions, long-running tasks/jobs, secure egress for tool calls, secrets and rate-limit management. - Managed agent platforms: AWS Bedrock AgentCore, Bedrock Agents / Knowledge Bases, SageMaker. - MCP (Model Context Protocol): authoring or hosting MCP servers/clients, exposing internal tools/data safely to agents.

A supportive, senior team with real problems and real users. Freedom to design and influence. Global collaboration and open exchange of ideas. And the chance to build a platform whose output shows up — directly — in better sleep, better breathing, and better health for millions of people. ResMed’s AI platform powers dozens of data scientists and a growing set of GenAI / Agentic AI products that touch patients, clinicians, and providers worldwide. We run on AWS and Kubernetes, provisioned with Terraform, and shipped through modern CI/CD. 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.

- Own CI/CD for data pipelines, ML models, and AI applications — from repo to production with confidence. - Stand up and evolve the platform observability stack — Prometheus, Loki, Grafana / Datadog — for metrics, logs, traces, dashboards, alerting, and SLOs. - Automate what shouldn’t be manual: environment provisioning, golden-path pipelines, self-serve tooling for data scientists.

- Working command of Terraform — modules, state, reviews, drift. - Platform observability experience: Prometheus, Loki, Grafana and/or Datadog — metrics, logs, dashboards, alerting, SLOs. - Strong production Python (and SQL for data work).

- 3+ years of engineering experience in a complex, technical environment. - Deep, hands-on Kubernetes in production. - Hands-on AWS — comfortable with 3+ of: EKS, Lambda, EC2, S3, IAM, Networking (VPC, ALB/NLB), RDS, EMR, Glue, Athena, Batch, SageMaker, MWAA/Airflow.

- Hands-on working experience with an AI/ML platform in production — data science tooling, model lifecycle, feature / inference infrastructure, and self-serve enablement for DS and GenAI teams. - Deploying AI agents / LLM workloads on Kubernetes — containerizing agent workloads, autoscaling (HPA/KEDA), GPU scheduling where needed, secure egress for tool calls, secrets and rate-limit management, and running long-lived / stateful sessions safely. - Exposure to the modern AI / Agentic AI stack is required — working familiarity with at least a few of: an agent framework (LangChain / LangGraph / CrewAI / AutoGen / Strands / Semantic Kernel / PydanticAI), LLM serving (vLLM, KServe, Ray Serve, TGI), a RAG / vector-store setup (OpenSearch, pgvector, Pinecone, Weaviate), LLM observability (Langfuse, LangSmith, Arize Phoenix, OpenTelemetry GenAI), and MCP (Model Context Protocol) for tool integration.

- AI / Agent frameworks: LangChain, LangGraph, Strands, or similar. - Running AI agents on Kubernetes: containerizing agent workloads, autoscaling (HPA/KEDA), stateful sessions, long-running tasks/jobs, secure egress for tool calls, secrets and rate-limit management. - Managed agent platforms: AWS Bedrock AgentCore, Bedrock Agents / Knowledge Bases, SageMaker.

- LLM/agent observability: Langfuse, LangSmith, Arize or OpenTelemetry GenAI — traces, evaluations, token / cost / latency tracking. - LLM serving on Kubernetes: vLLM, KServe, Ray Serve, TGI; GPU node pools and scheduling. - RAG stack: vector stores (OpenSearch, pgvector, Pinecone), embeddings pipelines, retrieval evaluation.

- Guardrails & safety: Bedrock Guardrails, prompt-injection defenses, PII redaction. - ML platform tooling: Kubeflow, MLflow, or comparable. - Snowflake and modern data stack experience.

- ML platform tooling: Kubeflow, MLflow, or comparable. - Snowflake and modern data stack experience. Why join

- Design, build, and operate the AI/ML platform on AWS + Kubernetes — clusters, networking, IAM, storage, cost, and reliability. - Provision and evolve infrastructure with Terraform; treat infra as code with real review and rollback. - Own CI/CD for data pipelines, ML models, and AI applications — from repo to production with confidence.

- Hands-on AWS — comfortable with 3+ of: EKS, Lambda, EC2, S3, IAM, Networking (VPC, ALB/NLB), RDS, EMR, Glue, Athena, Batch, SageMaker, MWAA/Airflow. - Working command of Terraform — modules, state, reviews, drift. - Platform observability experience: Prometheus, Loki, Grafana and/or Datadog — metrics, logs, dashboards, alerting, SLOs.