Senior MLOps Engineer

Role overview

You will be a Senior MLOps Engineer responsible for designing, building, and maintaining production-grade ML pipelines and infrastructure within an AWS-based data lakehouse ecosystem. You’ll collaborate with data engineers, data scientists, and DevSecOps teams to operationalize ML models end-to-end—covering data ingestion, training, deployment, and monitoring. The role is remote and works within a distributed agile environment.

Responsibilities

• Design and maintain ML Ops pipelines for model training, validation, and deployment across AWS environments.
• Automate model packaging, testing, deployment, and monitoring using CI/CD best practices.
• Operationalize ML workloads with data engineering and data science teams in the data lakehouse ecosystem.
• Build and maintain integrations between data ingestion, feature stores, and model repositories.
• Use Infrastructure as Code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure.
• Implement model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker Model Registry.
• Define and automate monitoring, alerting, and retraining strategies for deployed models.
• Ensure ML infrastructure meets enterprise security, compliance, and governance standards.
• Participate in code reviews, knowledge sharing, and continuous improvement of MLOps practices.
• Mentor junior engineers and contribute to documentation, standards, and best practices.

Requirements

• Bachelor’s degree in Computer Science/Data Engineering or equivalent experience.
• 4+ years professional experience in software, data, or ML engineering.
• 2+ years direct experience implementing and maintaining ML pipelines in production.
• Strong Python proficiency.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Hands-on AWS experience (SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM).
• CI/CD and containerization (Docker) experience.
• Experience building CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Infrastructure-as-Code experience (Terraform, AWS CDK, CloudFormation).
• Strong understanding of data pipelines, ETL/ELT, and feature engineering.

Nice to have / focus areas (from team description)

• ML pipeline automation & orchestration
• Model governance, observability, and lineage
• Feature store integration and reproducibility
• Secure, compliant, scalable ML infrastructure
• ML lifecycle automation improvements