Senior MLOps Engineer: Build & Scale ML Pipelines

Jobtailor; Ipswich (MA); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor in USA Massachusetts seeks an experienced ML Ops Engineer to design and implement scalable ML pipelines on AWS, covering training, deployment, and monitoring. You will automate packaging, testing, and deployment while collaborating with data engineers and scientists to operationalize workloads in a data lakehouse environment.

You will enforce CI/CD, apply infrastructure-as-code, and manage model versioning with MLflow or SageMaker Registry.

Qualifications

• Bachelor's degree in CS, data engineering or related field or equivalent experience.
• 4+ years of professional software, data, or ML engineering experience.
• 2+ years directly implementing ML pipelines in production.
• Strong Python skills and familiarity with ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Hands-on AWS experience (SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM).
• Solid CI/CD and containerization knowledge (Docker).
• Experience building CI/CD pipelines (Jenkins, Github Actions).
• Experience with infrastructure-as-code (Terraform, AWS CDK, CloudFormation).
• Understanding of data pipelines, ETL/ELT in a lakehouse environment.
• Ability to apply software engineering practices to ML workflows and collaborate well.

Responsibilities

• Design, build, and maintain ML Ops pipelines for training, validation, and deployment across AWS.
• Automate model packaging, testing, deployment, and monitoring using CI/CD best practices.
• Collaborate with data engineers/scientists to operationalize ML workloads in the data lakehouse.
• Develop integrations between data ingestion, feature stores, and model repositories.
• Apply infrastructure-as-code (Terraform, CDK, CloudFormation) to ML pipeline infra.
• Manage model versioning, reproducibility, and lineage with MLflow or SageMaker Registry.
• Define and automate monitoring, alerting, and retraining for deployed models.
• Ensure ML infra meets security, compliance, and governance standards.
• Participate in code reviews, knowledge sharing and continuous ML Ops improvement.
• Mentor junior engineers and contribute to documentation, standards, and best practices.

Skills

Python

ML Ops

AWS SageMaker

CI/CD

Docker

Terraform

AWS CDK

CloudFormation

GitHub Actions

Jenkins

Data Lakehouse

PyTorch

TensorFlow

Scikit-learn

MLflow

SageMaker Model Registry

Model Versioning

Education

Bachelor's Degree in Computer Science or Related Field

Tools

MLflow

SageMaker Model Registry

Jenkins

Github Actions

Data Lakehouse

Job description

Jobtailor in USA Massachusetts seeks an experienced ML Ops Engineer to design and implement scalable ML pipelines on AWS, covering training, deployment, and monitoring. You will automate packaging, testing, and deployment while collaborating with data engineers and scientists to operationalize workloads in a data lakehouse environment.

You will enforce CI/CD, apply infrastructure-as-code, and manage model versioning with MLflow or SageMaker Registry.