MLOps Engineer

Evlo AI; Atlanta (GA); On-site; USD 120,000 - 190,000; Full time

Job summary

Evlo AI is seeking an experienced MLOps/DevOps engineer to own the infrastructure and CI/CD pipelines powering its large-scale ML systems in Atlanta, GA. You will ensure smooth transitions from research notebooks to production services and collaborate with data scientists, ML engineers, and platform architects to build scalable, automated deployment workflows.

You will monitor model performance and data drift, optimize serving with Triton or TorchServe, and enforce security and governance for

Qualifications

• 3–6 years of experience in MLOps, DevOps, or reliability engineering with a heavy focus on machine learning workloads.
• Strong proficiency in Python, Bash, and infrastructure-as-code tools such as Terraform or CloudFormation
• Deep hands-on experience with containerization and orchestration platforms, particularly Docker and Kubernetes
• Familiarity with feature stores and model registries like Feast, Hopsworks, or MLflow
• Solid understanding of CI/CD pipelines for software and ML codebases (GitHub Actions, GitLab CI, ArgoCD)
• Bonus: Experience managing LLM inference pipelines, vLLM, TensorRT-LLM, or large-scale distributed training clusters

Responsibilities

• Design, build, and maintain robust MLOps infrastructure using Kubernetes, Docker, Terraform, and cloud-native tools
• Automate end-to-end model training, validation, and deployment pipelines utilizing tools like MLflow, Kubeflow, or AWS SageMaker
• Implement comprehensive monitoring systems to track model performance, latency, throughput, data drift, and concept drift in real time
• Optimize model serving architectures for low latency and high concurrency using Triton Inference Server or TorchServe
• Establish security, governance, and access control best practices for data storage, feature stores, and model artifact registries
• Collaborate with engineering teams to troubleshoot production incidents and continuously improve system reliability

Skills

Python

Bash

CI/CD pipelines

Tools

Kubernetes

Docker

Terraform

CloudFormation

MLflow

Kubeflow

AWS SageMaker

Feast

Hopsworks

TorchServe

Triton Inference Server

vLLM

TensorRT-LLM

ArgoCD

GitHub Actions

GitLab CI

Job description

About The Role
The role owns the infrastructure and CI/CD pipelines that power large-scale machine learning systems, ensuring models transition smoothly from research notebooks to resilient production services.

About The Role
The role owns the infrastructure and CI/CD pipelines that power large-scale machine learning systems, ensuring models transition smoothly from research notebooks to resilient production services.
The team works closely with data scientists, ML engineers, and platform architects to build scalable, automated, and observable AI deployment workflows.
Key Responsibilities

• Design, build, and maintain robust MLOps infrastructure using Kubernetes, Docker, Terraform, and cloud-native tools
• Automate end-to-end model training, validation, and deployment pipelines utilizing tools like MLflow, Kubeflow, or AWS SageMaker
• Implement comprehensive monitoring systems to track model performance, latency, throughput, data drift, and concept drift in real time
• Optimize model serving architectures for low latency and high concurrency using Triton Inference Server or TorchServe
• Establish security, governance, and access control best practices for data storage, feature stores, and model artifact registries
• Collaborate with engineering teams to troubleshoot production incidents and continuously improve system reliability

What We Are Looking For

• 3–6 years of experience in MLOps, DevOps, or reliability engineering with a heavy focus on machine learning workloads
• Strong proficiency in Python, Bash, and infrastructure-as-code tools such as Terraform or CloudFormation
• Deep hands-on experience with containerization and orchestration platforms, particularly Docker and Kubernetes
• Familiarity with feature stores and model registries like Feast, Hopsworks, or MLflow
• Solid understanding of CI/CD pipelines for software and ML codebases (GitHub Actions, GitLab CI, ArgoCD)
• Bonus: Experience managing LLM inference pipelines, vLLM, TensorRT-LLM, or large-scale distributed training clusters