{"id":201898,"url":"https://alion.io/job/resmed-machine-learning-ops-engineer","title":"Machine Learning Ops Engineer","company":{"id":99683,"name":"ResMed","domain":"resmed.co.uk","url":"https://alion.io/company/resmed-2","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":45,"ghost_share":0,"stale_share":0.8,"repost_share":0.022,"time_to_fill_p50_days":37,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":52000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Datadog","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Grafana","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"KEDA","optional":false},{"name":"KServe","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Loki","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prometheus","optional":false},{"name":"Pydantic AI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Ray Serve","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"TGI","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon EC2","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Bedrock AgentCore","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Embeddings","optional":true},{"name":"Kubeflow","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"MLFlow","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Ray","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-09-04T16:10:16Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-21T08:17:49Z","board_verified":true,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":46},"description":"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.\nThe 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.\nAbout the role\nResMed’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.\nWe are looking for AI/ML PlatformEngineer whose core is Kubernetes, AWS, Terraform, AI and platform observability - someone who can design, build, and operatethe 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 LLMOpsand Agentic AI as those workloads scale.\nWhat you’lldo\nDesign, build, and operatethe AI/ML platform on AWS + Kubernetes - clusters, networking, IAM, storage, cost, and reliability.\n\nProvision and evolve infrastructure with Terraform; treat infra as code with real review and rollback.\n\nOwn CI/CD for data pipelines, ML models, and AI applications - from repo to production with confidence.\n\nStand up and evolve the platform observability stack - Prometheus, Loki, Grafana / Datadog - for metrics, logs, traces, dashboards, alerting, and SLOs.\n\nAutomate what shouldn’tbe manual: environment provisioning, golden-path pipelines, self-serve tooling for data scientists.\n\nPartner with product, data science, and GenAI teams to make their workloads first-class on the platform - model serving, evaluation, cost/latency controls, and safe rollout.\n\nRun POCs to pull promising tech into the platform without accumulating debt.\n\nParticipate in code review, mentoring, and process improvement; raise the engineering bar.\n\nWhatwe’relooking for\nMust-have\n3+ years of engineering experience in a complex, technical environment.\n\nDeep, hands-on Kubernetes in production.\n\nHands-on AWS - comfortable with 3+ of: EKS, Lambda, EC2, S3, IAM, Networking (VPC, ALB/NLB), RDS, EMR, Glue, Athena, Batch, SageMaker, MWAA/Airflow.\n\nWorking command of Terraform - modules, state, reviews, drift.\n\nPlatform observability experience: Prometheus, Loki, Grafana and/or Datadog - metrics, logs, dashboards, alerting, SLOs.\n\nStrong production Python (and SQL for data work).\n\nExperience building CI/CD pipelines and APIs end-to-end - GitHub / GitHub Actions, CodePipelineor Jenkins.\n\nHands-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.\n\nDeploying 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.\n\nExposure 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, ArizePhoenix, OpenTelemetryGenAI), and MCP (Model Context Protocol) for tool integration.\n\nNice-to-have - AI / Agentic AI skills\nAI / Agent frameworks: LangChain, LangGraph, Strands, or similar.\n\nRunning AI agents on Kubernetes: containerizing agent workloads, autoscaling (HPA/KEDA), stateful sessions, long-running tasks/jobs, secure egress for tool calls, secretsand rate-limit management.\n\nManaged agent platforms: AWS Bedrock AgentCore, Bedrock Agents / Knowledge Bases, SageMaker.\n\nMCP (Model Context Protocol): authoring or hosting MCP servers/clients, exposing internal tools/data safely to agents.\n\nLLM/agent observability: Langfuse, LangSmith, Arizeor OpenTelemetryGenAI - traces, evaluations, token / cost / latency tracking.\n\nLLM serving on Kubernetes: vLLM, KServe, Ray Serve, TGI; GPU node pools and scheduling.\n\nRAG stack: vector stores (OpenSearch, pgvector, Pinecone), embeddings pipelines, retrieval evaluation.\n\nGuardrails & safety: Bedrock Guardrails, prompt-injection defenses, PII redaction.\n\nML platform tooling: Kubeflow, MLflow, or comparable.\n\nSnowflake and modern data stack experience.\n\nWhy join\nA 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.\nResMed’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.\nJoining 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! 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