Home › Companies › C The Signs › Senior MLOps Engineer
Senior MLOps Engineer
C The Signs · United States (Remote) · Remote · Active · Workable
Job facts
| Field | Value |
|---|---|
| Company | C The Signs |
| Title | Senior MLOps Engineer |
| Normalized title | - |
| Department / team | Other |
| Location | United States |
| Work model | Remote / Remote |
| Employment type | Full Time |
| Salary | - |
| Status | active |
| ATS provider | Workable |
| Posted / first seen | 2026-03-04 / 2026-05-31 |
| Changed / last seen | 2026-05-31 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from C The Signs. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Workable. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| Department jobs | Active postings in Other. | Open |
| Work model jobs | Active Remote postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | C The Signs |
| Source | c0cdce61-3e3d-481f-ac34-799da9c624b4 |
| ATS provider | Workable |
Description
Description
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.
Full job record
| Job ID | a44ecbf8f6fcb0eab8e95f011abb7ee226b2e520 |
| Org ID | 80a156fe-fe28-40ac-8f76-79f62cd700cf |
| Source ID | c0cdce61-3e3d-481f-ac34-799da9c624b4 |
| Board ID | c0cdce61-3e3d-481f-ac34-799da9c624b4 |
| Provider | workable |
| Provider Job Key | 070D1EB209 |
| Title | Senior MLOps Engineer |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | United States (Remote) |
| Department | Other |
| Team | — |
| Employment Type | full_time |
| Workplace Type | remote |
| Remote Policy | remote |
| Country | United States |
| Region | — |
| City | — |
| Salary Raw | Description 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. |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://apply.workable.com/c-the-signs/jobs/view/070D1EB209 |
| Apply URL | https://apply.workable.com/c-the-signs/j/070D1EB209/apply |
| First Seen At | 2026-05-31 17:47:30Z |
| Last Seen At | 2026-06-06 13:32:14Z |
| Last Checked At | 2026-06-06 13:32:14Z |
| Last Changed At | 2026-05-31 17:47:30Z |
| Inactive At | — |
| Source Posted At | 2026-03-04 00:00:00Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=workable/board=c-the-signs/date=2026-06-06/2026-06-06T13-32-14-201Z-1d7e9089a1f44896e8046ec330c66acb780333ce7f00506c73e19318243ac2e3.json |
Event Fields
{
"content_hash": "13237fa58a27dc81fedbf510d4c1d79c4e7e33f0bee4b35744aa09f2bcaab718",
"source_hash": "5da2fe4ab549adb045a254e24cc6ddd290d7d494d3ad71161e295dccf66f0e1b",
"last_changed_at": "2026-05-31T17:47:30.512Z",
"active_status": "active"
}Parsed Structured
{
"language": "en",
"location": {
"raw": "United States (Remote)",
"city": null,
"region": null,
"country": "United States",
"is_remote": true,
"confidence": 0.95
},
"salary_max": null,
"salary_min": null,
"inferred_at": "2026-06-06T13:32:14.643Z",
"launch_scope": {
"reason": "english_us_canada",
"included": true,
"language": "en",
"location": {
"raw": "United States (Remote)",
"city": null,
"region": null,
"country": "United States",
"is_remote": true,
"confidence": 0.95
},
"countries": [
"United States"
]
},
"remote_policy": "remote",
"salary_period": null,
"workplace_type": "remote",
"salary_currency": null
}Extensions
{}Native Structured
{
"detail": {
"type": "Full-time",
"title": "Senior MLOps Engineer",
"posted": "2026-03-04",
"company": "C the Signs",
"applyUrl": "https://apply.workable.com/c-the-signs/j/070D1EB209/apply",
"location": "United States (Remote)",
"workplace": "remote",
"department": null,
"descriptionText": "Description\n\n Position Summary\n\nWe’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.\n\nThis 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.\n\n Key responsibilities\n\n MLOps & ML Platform\n\n Design and operate ML platforms that support end to end workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring.\n Build and maintain CI/CD for ML (testing, packaging, versioning, reproducibility, automated rollbacks, approvals).\n Implement MLOps best practices: model registry, experiment tracking, lineage, governance, and reproducible training environments.\n Develop scalable training infrastructure (distributed training, GPU scheduling, cost controls, auto scaling).\n Create and maintain feature pipelines / feature stores, ensuring consistency between training and inference (training serving skew prevention).\n Establish model monitoring and observability: performance, drift, bias/fairness signals (where relevant), latency, throughput, and data quality.\n Build and own end to end LLM delivery pipelines: prompt/versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvement.\n Create robust LLM evaluation harnesses (offline + online): golden datasets, automated regression testing, human in the loop review workflows, and risk scoring.\n Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning. \n \n \n \n\n Deployment, reliability, and operations\n\n 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.\n Implement observability: tracing, metrics, logs, dashboards, alerting for model/system health (latency, token usage, error rates, retrieval quality, hallucination indicators, drift where relevant).\n Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning. \n \n \n\n Data, governance, and compliance (Healthcare)\n\n Design systems with security and privacy by default: IAM, least privilege, secrets management, audit logs, encryption, data retention, and PHI/PII handling.\n Implement governance: model/prompt lineage, dataset provenance, evaluation traceability, and approval workflows aligned with healthcare compliance expectations.\n\nIntegrate guardrails: content filters, policy checks, prompt injection defenses, structured output validation, and fallback strategies.\n\n Requirements\n\n 6+ years in software/platform engineering, including 4+ years operating ML systems in production (or equivalent depth).\n Strong experience in ML engineering: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.\n Strong engineering skills in Python, plus production grade experience building APIs/services. \n \n \n Demonstrated hands on experience with LLM systems in production and ML engineering: training pipelines, evaluation, deployment patterns, monitoring, and iteration loops.\n Strong experience with GCP services and cloud native patterns.\n Experience with Vertex AI (pipelines, endpoints, feature store, model registry, evaluation) and/or managed vector search on GCP.\n Experience with containerization and orchestration (Docker, Kubernetes/GKE and/or Cloud Run).\n\n Benefits\n\n Why Join Us? \n\nJoining 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.\n\n Benefits: \n\n Competitive salary and benefits package.\n Flexible working arrangements (remote or hybrid options available).\n The opportunity to work on life changing AI technology that directly impacts patient outcomes.\n Join a team that combines cutting edge innovation with a mission to save lives and improve health equity.\n Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare."
},
"list_job": {
"id": "070D1EB209",
"type": "Full-time",
"title": "Senior MLOps Engineer",
"posted": "2026-03-04",
"salary": null,
"location": "United States (Remote)",
"detailUrl": "https://apply.workable.com/c-the-signs/jobs/view/070D1EB209.md",
"department": "Other"
},
"detail_meta": {
"url": "https://apply.workable.com/c-the-signs/jobs/view/070D1EB209.md",
"http_status": 200,
"content_type": "text/markdown; charset=utf-8",
"response_bytes": 4888
},
"detail_errors": []
}Get this page with API
Rendered from the bluedoor Job Postings API. Reproduce it:
GET https://api.bluedoor.sh/job-postings/v1/jobs/a44ecbf8f6fcb0eab8e95f011abb7ee226b2e520?include=descriptionJSONGET https://api.bluedoor.sh/job-postings/v1/orgs/80a156fe-fe28-40ac-8f76-79f62cd700cfJSONGET https://api.bluedoor.sh/job-postings/v1/sources/c0cdce61-3e3d-481f-ac34-799da9c624b4JSONGET https://api.bluedoor.sh/job-postings/v1/jobs/a44ecbf8f6fcb0eab8e95f011abb7ee226b2e520/eventsJSON