Senior MLOps Engineer
At a glance
Senior ML Platform & Ops role at C the Signs. Remote, United States · full-time · no salary stated · we estimate $180,000–$245,000 across the United States.
C the Signs has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run ML platforms from data intake and feature work through training, evaluation, deployment and monitoring
- Create CI/CD workflows for ML and LLM systems, with automated checks, version control, approvals and safe rollbacks
- Deliver LLM pipelines covering prompts, retrieval, orchestration, evaluations, deployment and ongoing refinement
- Track model and system health, including drift, latency, data quality, retrieval quality and error rates
- Apply healthcare safeguards through access controls, audit trails, data protection, governance processes and LLM guardrails
What you bring
- At least six years in software or platform engineering, including four years running production ML systems or equivalent depth
- Hands-on production experience with LLMs, plus strength across ML training, evaluation, release, monitoring and iteration
- Strong Python skills and experience building APIs or services for production use
- Experience with GCP and cloud-native systems, including Vertex AI or managed vector search on GCP
- Experience packaging and orchestrating workloads with Docker, Kubernetes/GKE or Cloud Run
Who this fits
This senior role suits an engineer who has operated ML in production and wants to build LLM delivery and evaluation systems for healthcare workflows. It is listed as a full-time remote role in the United States.
From the employer
Position Summary
We’re hiring a Senior MLOps Engineer with deep machine learning engineering experience to build and operate the production platform powering ML/LLM-driven healthcare workflows. You’ll design reliable, secure, and compliant systems for model development, evaluation, deployment, monitoring, and continuous improvement—working closely with ML, data, security, and product teams.
This role is ideal for someone who has shipped ML systems in production and is excited about LLM orchestration, RAG, evaluations, guardrails, and observability in a regulated environment.
Key responsibilities
MLOps & ML Platform
- 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.
Deployment, reliability, and operations
- 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).
- Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning.
Data, governance, and compliance (Healthcare)
- 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).
Benefits
Why Join Us?
Joining C the Signs is not just about building AI; it’s about shaping the future of healthcare. If you are a technical leader with an unshakable belief in the power of AI to save lives and the ability to make it happen at scale, this is your opportunity to create a tangible, global impact.
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.