Senior MLOps Engineer

Location: 100% Remote
Duration: 5 Months Contract
Employment Type: W2 Only
Rate: Up to $90/hr (flexible for the right candidate)

Overview

We are seeking a Senior MLOps Engineer to help scale and operationalize machine learning platforms supporting predictive analytics and AI-driven applications. This role sits at the intersection of Data Science, DevOps, Cloud Infrastructure, and Platform Engineering, focused on taking models from experimentation through fully automated, monitored, and production-ready deployments.

The ideal candidate will have deep expertise in AWS SageMaker, Terraform, CI/CD automation, and ML platform operations, along with a strong understanding of machine learning concepts and model lifecycle management.

Required Qualifications

• 5+ years of experience in MLOps, ML Platform Engineering, or ML Infrastructure Engineering with ownership of production ML systems.
• Deep AWS expertise, including:
  • SageMaker (training jobs, pipelines, model registry, endpoints)
  • S3
  • IAM
  • KMS
  • CloudWatch
  • Lambda
  • Step Functions
  • Multi-account AWS environments
• Strong DevOps and Infrastructure-as-Code experience using:
  • Terraform
  • GitLab CI/CD, GitHub Actions, or similar tools
  • Docker
  • Git workflows
• Strong understanding of machine learning fundamentals, including:
  • Model training and evaluation
  • Feature engineering
  • AUC, calibration metrics, C-index, and performance monitoring
• Advanced Python development experience with production-quality, well-tested code.
• Experience with model monitoring, drift detection, and model lifecycle management.
• Strong security knowledge, including least-privilege access controls, encryption, and secrets management.

Key Responsibilities

ML Platform & Lifecycle Management

• Design, build, and maintain end-to-end ML training and inference pipelines.
• Manage model registry, versioning, validation, promotion, and production deployments.
• Implement blue/green deployment strategies and automated rollback mechanisms.

Infrastructure as Code

• Develop and maintain Terraform modules supporting:
  • SageMaker
  • S3
  • KMS
  • IAM
  • CloudWatch
  • Cross-account AWS infrastructure
• Support development, staging, and production environments.

CI/CD Automation

• Design and maintain CI/CD pipelines covering:
  • Automated testing
  • Infrastructure security scanning
  • Static code analysis
  • Dependency validation
  • Terraform plan/apply workflows
  • Automated model promotion

Production Operations

• Build observability solutions using CloudWatch dashboards, metrics, logging, and alerting.
• Implement model and data drift detection strategies.
• Improve reliability, performance, and operational support for ML services.

Security & Governance

• Implement secure cross-account access patterns.
• Manage encryption, secrets, model artifact integrity, and compliance controls.
• Monitor infrastructure and AI workload costs.

AI & LLM Operations

• Support deployment and management of LLM-powered workloads.
• Optimize throughput, cost efficiency, monitoring, and operational guardrails for generative AI services.

Data Science Partnership

• Collaborate closely with Data Scientists to productionize experimental models.
• Establish reusable MLOps standards, best practices, and deployment patterns.

AIOps & Automation

• Implement anomaly detection across model, infrastructure, and cost signals.
• Design automated remediation workflows, including scaling and rollback mechanisms.
• Leverage AI-assisted observability and incident analysis to reduce operational overhead and improve MTTR.

Preferred Experience

• AWS Bedrock
• XGBoost and predictive modeling workloads
• Model governance and ML platform standardization
• AIOps and intelligent monitoring solutions
• Enterprise-scale, multi-account AWS environments
• Healthcare, clinical, or regulated industry experience

Top Skills

• AWS SageMaker
• Terraform
• Python
• GitLab CI/CD
• MLOps
• Model Monitoring & Drift Detection
• Docker
• IAM/KMS Security
• CloudWatch
• AWS Bedrock

Keywords: MLOps Engineer, ML Platform Engineer, SageMaker Engineer, AWS AI Engineer, Machine Learning Infrastructure Engineer, Terraform, CI/CD, Python, Bedrock, Model Deployment, Data Science Platform, ML Operations.