Skills

Python CI/CD DevOps Docker Kubernetes Monitoring Version Control Jenkins Training Machine Learning git AWS Numpy Pandas AWS Cloud Data Science CI/CD Pipelines Terraform Infrastructure as Code

Job Specifications

Please find the position details below:

Job Title: Machine Learning Ops Engineer

Location: Durham, NC, Jersey City, NJ or Westlake TX (Hybrid – 2 weeks onsite , 2 weeks remote)Duration: Long term Contract with possibility of Conversion

Interview: One round: 60-minute panel interview

What Is the Client Looking For?

The client is strictly looking for a Senior ML Ops Engineer, not a Data Scientist.

They want someone who:

Strong Python Engineer (OOP-Focused)

5+ years of Python development
Object-oriented design
ML ecosystem familiarity (NumPy, pandas, etc.)
Production-grade coding

Deep AWS Cloud Expertise

Hands-on experience with services like:

Lambda
Step Functions
CloudFormation
IAM
Glue
S3
SNS / SQS
SageMaker (very important)
CloudWatch
EventBridge
AWS Batch

This is a cloud-native AWS engineering role, not just general cloud exposure.

ML Ops & SageMaker Experience (Critical)

Deploying ML models using SageMaker
Managing training jobs
Model hosting
Monitoring
Scaling production endpoints

They want someone who has operationalized ML models at scale, not someone who only experimented with ML.

Infrastructure as Code (IaC)

CloudFormation (required)
Terraform/OpenTofu (nice to have)
Ability to provision environments programmatically

CI/CD & Automation

Jenkins pipelines
Git version control
Automated deployment pipelines
DevOps mindset

Containerization & Kubernetes

Docker
Kubernetes (especially EKS)
Application hosting in containerized environments

The client wants a cloud-native AWS ML platform engineer who builds scalable infrastructure, CI/CD pipelines, and automated deployment systems for machine learning models — not someone who builds the models themselves.

What Is the Project?

This project is focused on building and enhancing an enterprise-level ML platform for the Enterprise Data Science Platform team.

The goal is not to build machine learning models, but to build the infrastructure, automation, and cloud platform that allows Data Scientists to:

Train models
Deploy models
Scale models to millions of users
Monitor model performance
Manage feature generation and storage
Automate CI/CD for ML pipelines

It’s a large-scale, production-grade ML platform engineering project in a financial services environment.

In short:

Build the engine and factory that runs ML — not the ML models themselves.

About the Company

Compunnel, Inc., founded in 1994 and headquartered in New Jersey, is at the forefront of providing customized workforce and digital transformation solutions, enhancing the total experience for our valued business allies. Our approach integrates innovative technologies with a human-centric dialogue, exceeding the needs of our customers, employees, partners, and key growth contributors and delivering exceptional value. With a strategic presence across the United States through over 30 local service delivery centers and glob... Know more