Senior MLOps Engineer

C the Signs; United States; Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary and benefits

Flexible working arrangements

Continuous learning opportunities

Job summary

A healthcare technology company in the United States seeks a Senior MLOps Engineer to design and operate machine learning platforms for healthcare workflows. The ideal candidate will have strong Python skills and significant experience with ML systems and GCP services. They will lead efforts to implement observability, governance, and security measures that align with healthcare compliance, driving innovations that can directly impact patient outcomes. Flexibility in work arrangements is offered.

Qualifications

• 6+ years in software/platform engineering, including 4+ years operating ML systems.
• Strong engineering skills in Python with production-grade experience.
• Demonstrated hands-on experience with LLM systems in production.

Responsibilities

• Design and operate ML platforms that support end-to-end workflows.
• Productionize ML Models on GCP using containers and orchestration.
• Implement governance for healthcare compliance and security.

Skills

Machine Learning engineering

Python

CI/CD for ML

GCP services

Containerization

APIs/services

LLM systems

Tools

Docker

Kubernetes

Vertex AI

Job 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.

• 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).

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.