Senior ML Ops Engineer

KellyMitchell Group; Minneapolis (MN); Remote; USD 189,000 - 270,000; Full time

Benefits offered by this job

Medical, Dental & Vision

Employee-Owned ESOP

401K

Job summary

KellyMitchell Group is seeking a Sr. ML Ops Engineer for a 12-month contract (potential to convert) to deploy and manage ML solutions in AWS. This remote role leads cross-functional teams to deliver scalable AI systems.

You will own the end-to-end deployment lifecycle, implement CI/CD for ML, and design secure AWS architectures using SageMaker, Lambda, API Gateway, and CloudWatch. Strong engineering fundamentals and enterprise security experience are required.

Qualifications

• Hands-on experience deploying ML models in production AWS environments.
• Strong experience with SageMaker, Lambda, API Gateway, and CloudWatch.
• Hands-on Terraform provisioning and management of AWS infrastructure.
• Experience building CI/CD pipelines for ML or cloud deployments.
• Understanding of model packaging, hosting, inference endpoints, and app integration.
• Experience troubleshooting production ML systems.
• Knowledge of AWS networking, IAM, VPC, and enterprise security.
• Strong software engineering fundamentals and collaboration with cloud teams.
• Ability to communicate technical concepts and document solutions.
• Experience in large enterprise environments with security controls.

Responsibilities

• Deploy and maintain ML solutions in AWS and production apps.
• Own end-to-end ML deployment lifecycle from onboarding to monitoring.
• Design scalable, secure AWS architectures for ML workloads.
• Configure AWS services for hosting, connectivity, monitoring, and performance.
• Collaborate with application, data, and engineering teams.
• Monitor production ML solutions and troubleshoot to ensure availability.
• Support scalable engineering practices and deployment standards.
• Partner with stakeholders to evaluate requirements and drive ML implementations.

Skills

ML Ops

AWS

SageMaker

Terraform

CI/CD

Model hosting

Monitoring

Security

Networking/IAM

Communication

Tools

SageMaker

Lambda

API Gateway

CloudWatch

Terraform

ECR

Job description

Job Summary

Our client, a leading Financial Services provider, is seeking a Sr. ML Ops Engineer to support the deployment, automation, and operational management of machine learning solutions within AWS environments. This role will own the end-to-end ML deployment lifecycle, partnering with cross-functional teams to deliver scalable, secure, and reliable production AI solutions.

Contract details

Contract type and duration: 12 Month contract -potential to convert Location: USA Work Model: Remote

Core Responsibilities

• Deploy, configure, and maintain machine learning solutions within AWS environments, ensuring reliable integration into production applications
• Own the end-to-end ML deployment lifecycle, including model onboarding, infrastructure setup, deployment, validation, and operational support
• Design, implement, and document AWS architectures that support scalable, secure, and high-performing ML workloads
• Configure and manage AWS services required for model hosting, application connectivity, monitoring, and performance optimization
• Collaborate with application, data, and engineering teams to enable seamless consumption of deployed ML models
• Monitor production ML solutions, troubleshoot issues, and implement improvements to ensure availability and performance
• Support the establishment of scalable engineering practices and deployment standards within a growing AI organization
• Partner with cross-functional stakeholders to evaluate requirements, recommend technical solutions, and drive successful ML implementations

Required Skills/Experience (Must-Haves)

• Proven hands-on experience personally deploying and supporting machine learning models in production AWS environments
• Strong experience with AWS services, particularly SageMaker, Lambda, API Gateway, and CloudWatch
• Hands-on Terraform experience provisioning and managing AWS infrastructure
• Experience building or maintaining CI/CD pipelines for machine learning or cloud application deployments
• Understanding of model packaging, hosting, inference endpoints, and application integration
• Experience troubleshooting production systems and supporting deployed ML applications
• Knowledge of AWS networking, IAM, VPC configurations, and enterprise security requirements
• Strong software engineering fundamentals and experience collaborating with cloud and platform engineering teams
• Ability to communicate technical concepts, explain architectural decisions, and document solutions
• Experience working within large enterprise environments with established security and infrastructure controls

Preferred Skills/Experience (Nice-to-Haves)

• AWS or Terraform certifications
• 5+ years in software, cloud, or data engineering
• 2+ years deploying ML solutions in AWS
• Docker containerization and Amazon ECR experience
• Experience with SageMaker Model Registry, Model Monitor, and model versioning
• Familiarity with blue/green, canary, or other controlled deployment strategies
• Experience optimizing AWS infrastructure for scalability, reliability, and cost
• Experience creating reusable MLOps templates or deployment frameworks
• Exposure to LLM integration within production ML workflows

Compensation & Benefits

• Pay Range: The approximate pay range for this position is between $189,000 and $270,000. Please note that the pay range provided is a good faith estimate. Final compensation may vary based on factors including but not limited to background, knowledge, skills, and location. We comply with local wage minimums.
• Medical, Dental, & Vision Insurance Plans
• Employee-Owned Profit Sharing (ESOP)
• 401K offered

About KellyMitchell

At KellyMitchell, our culture is world class. We’re movers and shakers! We don’t mind a bit of friendly competition, and we reward hard work with unlimited potential for growth. This is an exciting opportunity to join a company known for innovative solutions and unsurpassed customer service. We're passionate about helping companies solve their biggest IT staffing & project solutions challenges. As an employee-owned, women-led organization serving Fortune 500 companies nationwide, we deliver expert service at a moment's notice.