MLOps Engineer (Remote)

Cedent; United States; Remote; USD 55,104 - 82,656; Full time

Benefits offered by this job

Health Insurance

Dental Insurance

Vision Insurance

Job summary

A leading cloud solutions provider is looking for an experienced MLOps Engineer to manage the lifecycle of production machine learning models. You will be responsible for building pipelines, monitoring production models, and ensuring performance and monitoring standards are met. The ideal candidate will have GCP experience, strong Python skills, and the ability to work remotely during CST hours. This position offers an hourly rate of $40–$60 and includes health benefits.

Qualifications

• 4+ years of MLOps or DevOps for ML systems.
• Hands-on experience with Google Cloud Platform.
• Proficient in Python and CI/CD methodologies.

Responsibilities

• Design CI/CD pipelines for training and inference.
• Standardize model packaging and rollout strategies.
• Implement monitoring and alerting for models.

Skills

MLOps/ML platform experience

GCP experience

Python proficiency

SQL skills

Tools

Docker

Terraform

Job description

Project Details

Own the end-to-end lifecycle of production ML: training, packaging, deployment, monitoring, and governance. Build reusable pipelines and tooling so data scientists and contractors can ship reliable model quickly – batch and real-time – on Google Cloud.

Must Have Skills

• 4+ years of MLOps/ML platform or DevOps for data/ML systems
• Hands on GCP experience: BigQuery, Cloud Run, Cloud Storage, Pub/Sub, Cloud Build (Vertex AI a plus)
• Proficiency with Python, packaging (Docker), and CI/CD
• Solid SQL skills and understanding of data modeling for ML features/labels
• Experience operating production models with monitoring, alerting, and incident response

Nice to have Skills

• Model registry & experiment tracking (ML Flow, W&B, or Vertex AI)
• Data validation & monitoring (Great Expectations, TensorFlow Data Validation, WhyLabs, Arize)
• Feature store concepts (BQ-based or managed)
• Canary/shadow deployments, autoscaling, and performance tuning
• IaC (Terraform), testing frameworks (unit/integration/lead), and observability (Open Telemetry, Cloud Monitoring)

Education/Certification Requirements

• N/A

Day to Day Responsibilities

• Pipelines & orchestration: Design CI/CD and scheduled pipelines for training and inference (Cloud Build, Workflows/Scheduler, Pub/Sub, Cloud Run; Vertex Pipelines if used).
• Packaging & deployment: Standardize model packaging (Docker), artifact/versioning, and rollout strategies (A/B, canary, shadow) with automated rollbacks.
• Data/feature flows: Define contracts for features/labels in BigQuery and manage backfills; support batch and (where applicable) streaming features.
• Registry & experimentation: Stand up a model registry and experiment tracking (MLflow/Weights & Biases/Vertex) with approvals and audit trails.
• Monitoring & quality: Implement data/feature validation, drift/decay monitoring, performance/latency SLOs, and alerting; build dashboards and playbooks.
• Security & compliance: Enforce IAM least privilege, service accounts, Secrets Manager, provenance/lineage, and change management.
• Cost & performance: Track training/inference cost and latency; optimize hardware/ autoscaling and query patterns.
• Enablement: Create templates, docs, and tooling so DS/contractors can add models with minimal friction.
• Data/Storage: BigQuery, Cloud Storage (artifacts, datasets)
• CI/CD & IaC: Cloud Build or GitHub Actions, Terraform
• ML Tooling: MLflow/W&B/Vertex, Docker, PyTorch/TF/XGBoost (as provided by DS)
• Monitoring: Cloud Logging/Monitoring, Evidently/WhyLabs/Arize, custom run IDs & metrics

Tech Stack you’ll use

• Compute/Orchestration: Cloud Run, Workflows/Scheduler, Pub/Sub, Vertex Pipelines (optional)
• Data/Storage: BigQuery, Cloud Storage (artifacts, datasets)
• CI/CD & IaC: Cloud Build or GitHub Actions, Terraform
• ML Tooling: MLflow/W&B/Vertex, Docker, PyTorch/TF/XGBoost (as provided by DS)
• Monitoring: Cloud Logging/Monitoring, Evidently/WhyLabs/Arize, custom run IDs & metrics

How we work

• Small, versioned releases; test-first pipelines; documented runbooks.
• Clear SLOs and blameless incident reviews.

Location

Remote (working in CST hours)

Compensation

Hourly Rate Range – $40–$60/hr

Benefits Offered

Health, Dental, Vision Insurance

Deadline

Applications accepted until 10/30/2025 at 11:59 PM CST

Equal Employment Opportunity Statement

We are an Equal Pay Employer. All employment decisions, including compensation, benefits, hiring, training, and promotions, are made based on merit, qualifications, and business needs. We do not discriminate on the basis of gender, race, ethnicity, age, disability, sexual orientation, or any other protected characteristic. We are committed to ensuring equal pay for equal work and regularly review our compensation practices to promote fairness, equity, and transparency across our organization.