Role Overview

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.

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).

Tech Stack

Cloud
Docker
Google Cloud Platform
Kubernetes
Python

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.

C the Signs

Open Positions (1)

Engineering

Senior MLOps Engineer