ML Ops Engineer

ob Title: MLOps Engineer AWS | Terraform | SageMaker

Experience: 3+ Years
Location: Remote / Offshore (India) Work hours aligned to US Pacific Time (approx. 8:00 PM – 5:00 AM IST)
Employment Type: Full-Time, Permanent
Role Category: IT Consulting / Cloud Consultant
Industry: IT Services & Consulting

About the Role

We're hiring an MLOps Engineer to own the reliability, deployment, and monitoring of a production ML pipeline that powers daily risk scores used by operational leaders to prevent incidents before they happen. This is a hands-on infrastructure and operations role — the model is trained offline; your job is to keep the scoring pipeline dependable, observable, secure, and easy to evolve.

You'll work across a modern AWS stack (S3, Glue, SageMaker, Step Functions) backed by a fully automated Terraform + GitHub Actions delivery pipeline spanning multiple environments.

What You'll Do

• Operate and improve an end-to-end inference pipeline (SAP HANA AWS Glue SageMaker Step Functions SAP HANA), ensuring reliable scheduled daily runs
• Own infrastructure-as-code: maintain Terraform modules, manage remote state via Terraform Cloud, and promote changes across dev qa prd
• Build and maintain CI/CD workflows in GitHub Actions
• Ensure full pipeline observability through structured JSON logging, CloudWatch Logs Insights, and Step Functions execution monitoring
• Support model drift detection and data-quality checks; tune alert thresholds and triage failures
• Troubleshoot production incidents (DB connectivity, SageMaker job failures, Glue write-back, scheduler issues) using established runbooks and drive root-cause resolution
• Enforce security and compliance: encryption, least-privilege IAM, secrets management, and no-PII-in-logs discipline
• Partner with data scientists to deploy retrained models via SageMaker Model Registry and cross-account promotion

Must-Have Skills

• 3+ years of MLOps experience supporting production systems
• Strong hands-on AWS experience, especially data/ML services (Glue, SageMaker, Step Functions, S3, CloudWatch)
• Production Terraform experience with remote backend and multi-environment promotion
• Solid Python skills for data processing and operational scripting
• Proven experience operating CI/CD pipelines and diagnosing failures from logs/metrics
• Working knowledge of Docker/containerized environments

Technical Skills We'll Test/Value Most

AreaWeightKey ToolsAWS Cloud & ML Services35%Glue (PySpark, Data Catalog, crawlers), SageMaker (Processing Jobs, Model Registry), Step Functions (ASL), EventBridge Scheduler, S3, CloudWatch, Lake Formation, VPCInfrastructure as Code20%Terraform (modules, Terraform Cloud, multi-env config), AWS/AWSCC providersCI/CD & Automation15%GitHub Actions, composite actions, environment approvals, Git/PR workflows, MakePython & ML Tooling10%pandas, pyarrow, scipy, boto3, scikit-learn artifacts, pytest, Black, flake8Docker & Containerization10%Docker builds, containerized test execution, Glue/SageMaker managed containersObservability, Reliability & Security10%Structured JSON logging, drift/data-quality monitoring, SNS/SES alerting, IAM, encryption

Good to Have

• SHAP for model explainability, imbalanced-learn (SMOTE)
• Experience with SAP HANA integrations
• GitHub CLI / AWS Kiro exposure