C the Signs logo
C the Signs

C the Signs is a cancer prediction system that identifies patients at risk of cancer at the earliest, most curable stage

Senior MLOps Engineer

Machine Learning EngineerMachine Learning EngineerOtherRemoteSeniorTeam 51-200H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

102 days ago

Salary

0

Seniority

Senior

Bachelor Degree6 yrs expEnglishDockerGCPKubernetesPython

Job Description

Senior MLOps Engineer

C the Signs

• Design and operate ML platforms that support end-to-end workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring. • Build and maintain CI/CD for ML (testing, packaging, versioning, reproducibility, automated rollbacks, approvals). • Implement MLOps best practices: model registry, experiment tracking, lineage, governance, and reproducible training environments. • Develop scalable training infrastructure (distributed training, GPU scheduling, cost controls, auto-scaling). • Create and maintain feature pipelines / feature stores, ensuring consistency between training and inference (training-serving skew prevention). • Establish model monitoring and observability: performance, drift, bias/fairness signals (where relevant), latency, throughput, and data quality. • Build and own end-to-end LLM delivery pipelines: prompt/versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvement. • Create robust LLM evaluation harnesses (offline + online): golden datasets, automated regression testing, human-in-the-loop review workflows, and risk scoring. • Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning. • Productionize ML Models on GCP using containers and orchestration (e.g., GKE, Cloud Run), and build CI/CD for ML/LLM systems with automated tests and safe rollouts. • Implement observability: tracing, metrics, logs, dashboards, alerting for model/system health (latency, token usage, error rates, retrieval quality, hallucination indicators, drift where relevant). • Design systems with security and privacy by default: IAM, least privilege, secrets management, audit logs, encryption, data retention, and PHI/PII handling. • Implement governance: model/prompt lineage, dataset provenance, evaluation traceability, and approval workflows aligned with healthcare compliance expectations. • Integrate guardrails: content filters, policy checks, prompt injection defenses, structured output validation, and fallback strategies.

Job Requirements

  • 6+ years in software/platform engineering, including 4+ years operating ML systems in production (or equivalent depth).
  • Strong experience in ML engineering: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.
  • Strong engineering skills in Python, plus production-grade experience building APIs/services.
  • Demonstrated hands-on experience with LLM systems in production and ML engineering: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.
  • Strong experience with GCP services and cloud-native patterns.
  • Experience with Vertex AI (pipelines, endpoints, feature store, model registry, evaluation) and/or managed vector search on GCP.
  • Experience with containerization and orchestration (Docker, Kubernetes/GKE and/or Cloud Run).

Benefits

  • Competitive salary and benefits package.
  • Flexible working arrangements (remote or hybrid options available).
  • The opportunity to work on life-changing AI technology that directly impacts patient outcomes.
  • Join a team that combines cutting-edge innovation with a mission to save lives and improve health equity.
  • Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare.

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