MLOps Engineer.

Practovate; Hartford (CT); On-site; USD 120,000 - 180,000; Full time

Job summary

Practovate in Hartford, CT is seeking an experienced ML Engineer to design and run scalable ML pipelines. You will build end-to-end workflows, deploy models to production, and monitor data quality.

You will own CI/CD for ML, containerized services with Docker and ECS/Fargate, and collaborate with data engineers on Python PySpark and Snowflake pipelines. Strong AWS expertise is essential.

Qualifications

• Hands-on experience with AWS SageMaker.
• Experience setting up CI/CD for ML models (CodePipeline/CodeBuild).
• Strong Docker and ECS/Fargate expertise.
• Deploy, monitor, and lifecycle manage ML models.
• Develop data pipelines with Python and PySpark.
• Proficient in SQL and Bash.
• Familiar with Pandas, NumPy, PyTorch, Scikit-learn.
• Knowledge of AWS services: S3, IAM, Lambda, Step Functions, ECR.
• Experience with data/model quality monitoring.

Responsibilities

• Build and automate end-to-end ML pipelines.
• Deploy and manage ML models in production.
• Monitor model performance and data quality.
• Maintain CI/CD workflows and infrastructure.
• Ensure security, compliance, and system reliability.
• Continuously optimize performance and cost.

Skills

SageMaker

CI/CD for ML

Docker & ECS

ML model deployment

Python PySpark

Snowflake data pipelines

SQL

Bash

Pandas

NumPy

PyTorch

Scikit-learn

AWS S3 IAM Lambda Step Functions ECR

Tools

AWS CodePipeline

CodeBuild

Docker

ECS/Fargate

Snowflake

PySpark

SQL

Job description

++Job Description++

++Key Skills++

• Hands-on experience with Amazon SageMaker
• CI/CD setup for ML models using AWS CodePipeline & CodeBuild
• Strong experience with Docker and container orchestration (ECS/Fargate)
• ML model deployment, monitoring, and lifecycle management
• Data pipeline development using Python, PySpark, and Snowflake
• Proficiency in SQL and Bash/Shell scripting
• Experience with ML libraries: Pandas, NumPy, PyTorch, Scikit-learn
• Strong knowledge of AWS services: S3, IAM, Lambda, Step Functions, ECR
• Data and model quality monitoring experience

++Key Responsibilities++

• Build and automate end-to-end ML pipelines
• Deploy and manage ML models in production
• Monitor model performance and data quality
• Maintain CI/CD workflows and infrastructure
• Ensure security, compliance, and system reliability
• Continuously optimize performance and cost