Senior MLOps Engineer

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

• Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments.
• Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices.
• Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem.
• Develop and maintain integrations between data ingestion, feature stores, and model repositories.
• Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure.
• Implement and manage 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 all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards.
• Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices.
• Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.

Requirements

• Bachelor's Degree in Computer Science, Data Engineering, or a related technical field or equivalent experience.
• 4+ years of professional experience in software, data, or ML engineering.
• 2+ years of direct experience implementing and maintaining ML pipelines in production.
• Strong proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Hands-on experience with AWS services (SageMaker, Step Functions, Lambda, ECR, S3, Glue, IAM).
• Solid understanding of CI/CD, containerization (Docker).
• Experience with building CI/CD pipelines (Jenkins, Github Actions, etc.).
• Experience with infrastructure-as-code and automation (Terraform, AWS CDK, or CloudFormation).
• Strong understanding of data pipelines, ETL/ELT concepts, and feature engineering in a lakehouse environment.
• Proven ability to apply software engineering practices to machine learning workflows.
• Strong communication and collaboration skills across multidisciplinary teams.

Core Competencies

Demonstrates expertise in designing and maintaining ML Ops pipelines, utilizing AWS services and infrastructure-as-code tools to automate processes. Strong proficiency in Python and experience with ML frameworks, alongside a solid understanding of CI/CD practices and data engineering principles.

Highest-signal resume keywords

• ML Ops Pipeline Development
• AWS Services (SageMaker, Lambda, S3)
• Python Programming
• Infrastructure-as-Code (Terraform, CloudFormation)
• CI/CD Best Practices

ATS Optimization Keywords

Hard Skills

• ML Pipeline Implementation
• Python
• AWS CDK
• Terraform
• CI/CD
• Docker
• ETL/ELT Concepts
• Feature Engineering
• ML Frameworks (PyTorch, TensorFlow, Scikit-learn)
• Model Versioning

Soft Skills

• Communication
• Collaboration
• Mentoring

Certifications & Qualifications

• Bachelor's Degree in Computer Science or Related Field

Industry Keywords

• Machine Learning
• Data Engineering
• Model Deployment
• Automation
• Compliance

Tools & Technologies

• MLflow
• SageMaker Model Registry
• Jenkins
• Github Actions
• Data Lakehouse