Mlops DevOps

Staffxpert Llc
Atlanta, GA, United States
about 2 months ago
Apply on www.careerjet.com
Prepare application

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$105,000.0 - $160,000.0
Working hours
Regular working hours

Tech stack

Amazon Web Services Audit Trail Cloud Computing Cloud Computing Security Information Engineering DevOps Monitoring of Systems Identity and Access Management Python (Programming Language) Machine Learning Amazon Simple Notification Service (SNS) Management of Software Versions
+11 more
Snowflake Amazon Virtual Private Cloud (VPC) Containerization Pyspark Gitlab-ci Performance Monitor Machine Learning Operations Cloudwatch Terraform Data Pipelines Docker

Job description

  • Build and manage end-to-end MLOps pipelines using AWS SageMaker and SageMaker Domains.
  • Develop GitLab CI/CD pipelines for automated model build, testing, and deployment.
  • Automate AWS infrastructure using Terraform / IaC.
  • Containerize workloads using Docker and manage images through Amazon ECR.
  • Design secure AWS environments using VPC, IAM, Security Groups, and SNS.
  • Develop data pipelines using PySpark and integrate with Snowflake.
  • Implement SageMaker model monitoring for drift, performance, data quality, and alerts.
  • Support model deployment, versioning, rollback, monitoring, and troubleshooting.
  • Ensure security, governance, compliance, and auditability of MLOps workflows in a banking environment.
  • Collaborate with Data Science, ML Engineering, Data Engineering, Cloud, and Security teams.

Requirements

  • AWS SageMaker & SageMaker Domains
  • MLOps / ML Pipeline Development
  • GitLab CI/CD
  • Terraform / Infrastructure as Code
  • Docker & Amazon ECR
  • AWS VPC & IAM
  • Python & PySpark
  • Snowflake
  • SageMaker Model Monitoring (Drift & Performance)
  • AWS SNS / CloudWatch
  • Model Deployment & Governance
  • AWS Cloud Security & Compliance

Experience with AWS VPC, IAM, SNS, PySpark, Snowflake, and SageMaker Model Monitoring is required. The ideal candidate should also have experience with model drift/performance monitoring, cloud security, governance, auditability, and compliance, preferably within the banking or financial services industry. Key Skills: AWS SageMaker | SageMaker Domains | MLOps | GitLab CI/CD | Terraform | Docker | ECR | VPC | IAM | SNS | PySpark | Snowflake | Model Monitoring | Python | AWS Cloud Security.

Benefits & conditions

  • $105,000-160,000 per year Cargill’s size and scale allows us to make a positive impact in the world. Our purpose is to nourish the world in a safe, responsible and sustainable way. We are a family company p…

  • 10 days ago +

About the company

Cargill

  • Atlanta, GA
  • $105,000-160,000 per year Cargill’s size and scale allows us to make a positive impact in the world. Our purpose is to nourish the world in a safe, responsible and sustainable way. We are a family company p…, Cargill

  • Decatur, GA
  • $105,000-160,000 per year Cargill’s size and scale allows us to make a positive impact in the world. Our purpose is to nourish the world in a safe, responsible and sustainable way. We are a family company p…

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.careerjet.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

2:17 min

Mapping the maturity roadmap for scaled devops adoption

Dominik Krichbaum Dominik Krichbaum · World Congress 2026 Europe

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · World Congress 2023

2:34 min

Docker sandbox architecture and microVM environment integration

Manuel de la Peña Manuel de la Peña · World Congress 2026 Europe

3:37 min

Scaling machine learning pipelines from prototypes to petabytes

Julian Joseph · LIVE

Videos

See all

Related articles

See all