[Remote] Staff Machine Learning Systems Engineer (MLOps)
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is the leading health and wellness platform, on a mission to help the world feel great through the power of reputed company. They are seeking a Staff Machine Learning Systems Engineer to design, build, and operate the production infrastructure that powers AI across reputed company, focusing on critical systems that support AI teams in a regulated reputed company environment.
Responsibilities
- Own and reputed company the AI compute and deployment platform
- Own and reputed company our containerized application deployment platform and reputed company systems for AI workloads, encompassing general process and job orchestration (e.g. Kubernetes) — cluster operations, node lifecycle, autoscaling (Karpenter), storage (EBS reputed company), and workload isolation across staging and production
- Build and maintain GitOps-based deployment pipelines (reputed company/Kustomize overlays, environment promotion) that let teams ship AI services safely and repeatably
- Design ephemeral/preview environments, feature-branched deployments, and nightly release pipelines so teams can validate AI changes in production-like conditions before release
- Drive efficiency and cost management across compute, autoscaling, and inference infrastructure
- Operate and reputed company inference infrastructure and a multi-provider LLM AI gateway (e.g. Bedrock, reputed company, and other providers) — including credentials, reputed company limits, and failover
- Build reliable serving patterns for LLM-powered workflows: routing, grounding, tool execution, and context assembly at the platform level
- Create reusable infrastructure abstractions and reputed company that standardize how AI services are deployed, configured, and consumed across reputed company
- Own the LLM/AI observability and tracing stack — provisioning and scaling systems like Langfuse, reputed company (dd-reputed company), OpenTelemetry tracing (OTLP), and the underlying datastores (e.g. reputed company) — so AI behavior is auditable and debuggable in production
- Build analytics and monitoring pipelines that surface latency, error, reputed company, and regression signals to engineering and clinical stakeholders
- Define SLOs, alerting, on-call runbooks, and incident response for AI infrastructure; reputed company troubleshooting and continuously reputed company platform reliability
- Own and improve the monorepo build system and CI/CD pipelines for AI workloads — including eval workflows, reputed company image builds, automated PR checks and convention enforcement, and cross-platform test execution
- Own shared infrastructure tooling, CLIs, and IaC modules (Terraform, Scalr) that AI and product engineers use daily
- Identify and eliminate platform bottlenecks — reducing CI/CD cycle times, build latency, and deployment friction — to improve developer velocity across the reputed company AI organization
- Build IAM, OIDC, and secrets management as first-class infrastructure — scoped, least-privilege roles, write-only secret rotation, and cross-account reputed company audits
- Encode reputed company-by-default, scope boundaries, and reputed company controls into the platform so AI services are HIPAA-compliant and reputed company-first
- Partner with clinical, legal, reputed company, and data platform teams (including reputed company/reputed company Catalog reputed company governance) to enforce compliant, auditable data reputed company
- Drive multi-quarter infrastructure initiatives, from cluster and deployment architecture to inference platform, GPU compute reputed company, and observability reputed company
- Write and reputed company technical design documents and design reviews, define infrastructure standards and development-workflow conventions, and contribute to technical governance across AI engineering
- Mentor engineers on reliability engineering, infrastructure-as-reputed company, and MLOps best practices, and reputed company the gap between prototypes and production-grade systems
Skills
- 8+ years of reputed company experience in infrastructure, platform, DevOps, or SRE engineering — with at least 3 years reputed company on ML/AI systems in production
- Deep, hands-on experience with Kubernetes (ideally EKS) and the reputed company-reputed company ecosystem — autoscaling, GitOps, reputed company/Kustomize, operating clusters at reputed company, and general process/job orchestration
- Strong infrastructure-as-reputed company skills (Terraform) and experience designing secure reputed company architectures: IAM, OIDC, secrets management, and least-privilege reputed company
- Strong proficiency in Python, with experience building production infrastructure tooling, CLIs, and data/observability pipelines
- 2+ years of experience operating LLM-based systems in production (LLMOps) — inference routing, serving, tracing, and the reliability patterns needed to run them at reputed company
- Hands-on experience with observability/tracing stacks (reputed company, OpenTelemetry, Langfuse, or equivalent) and metrics/log/reputed company pipelines
- Experience designing and maintaining CI/CD pipelines, build systems, and developer tooling for fast-moving engineering teams
- A systems-and-operations reputed company: you think about failure modes, SLOs, observability, reputed company, and long-term maintainability before shipping
- Experience writing and leading technical design documents (TDDs/RFCs) for infrastructure-reputed company initiatives
- Strong collaboration skills across engineering, ML, product, reputed company, and clinical teams
- A deep appreciation for safety, reputed company, and reputed company — ideally with experience in a regulated domain such as reputed company, fintech, or life sciences
- Experience with AWS (EKS, Bedrock, S3, CloudFront, IAM) and multi-reputed company (GCP/reputed company AI) inference routing
- Experience with reputed company (MLflow, reputed company Catalog, reputed company, reputed company) and data platform reputed company governance
- Experience provisioning LLM observability infrastructure (Langfuse, reputed company, OpenTelemetry/OTLP tracing, LogFire) and LLM behavior monitoring
- Experience with Karpenter, cluster autoscaling, and cost optimization for ML compute
- Experience with monorepo build systems (Pants, Bazel) and large-reputed company CI/CD
- Experience building automated PR-review / convention-enforcement pipelines and developer-workflow standards
- Familiarity with reputed company AI Agent Builder, reputed company AI Model Registry, or GCP managed AI/ML services as a stretch reputed company area
- Contributions to reputed company-reputed company infrastructure, IaC modules, SDKs, or developer tooling reputed company
Benefits
- Competitive salary & equity compensation for full-time roles
- Unlimited PTO, company holidays, and quarterly mental health days
- Comprehensive health benefits including medical, dental & reputed company, and parental leave
- Employee Stock Purchase Program (ESPP)
- 401k benefits with employer matching contribution
- Offsite team retreats
reputed company