Machine Learning Engineer

Evlo AI; Raleigh (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI is seeking an experienced ML Engineer to own the design, implementation, and scaling of production ML systems powering enterprise applications. You will build end-to-end pipelines, deploy models on AWS, and ensure reliability at scale.

The role emphasizes collaboration with backend engineers and product teams, strong Python and PyTorch skills, and hands-on experience with cloud platforms. You will contribute to robust monitoring and CI/CD practices.

Qualifications

• 3 to 6 years of professional experience in machine learning engineering or applied software development.
• Strong proficiency in Python and hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow.
• Demonstrated track record of deploying and maintaining ML models in production environments using cloud platforms like AWS or GCP.
• Solid foundation in software engineering principles, CI/CD pipelines, and microservices architecture.
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field.
• Bonus: Experience with LLM orchestration frameworks, vector databases, or large-scale distributed computing systems.

Responsibilities

• Architect and deploy production-grade machine learning models using Python, PyTorch, and cloud infrastructure on AWS
• Build robust data ingestion and feature engineering pipelines using Apache Spark and SQL to support continuous model training
• Optimize model serving architectures for low latency and high availability, utilizing containerization tools like Docker and Kubernetes
• Implement comprehensive monitoring systems to track data drift, concept drift, and system performance regressions in real time
• Collaborate with backend engineers and product teams to integrate machine learning capabilities seamlessly into core software services
• Contribute to internal engineering standards by writing clean, well-tested code and participating in rigorous peer code reviews

Skills

Python

Machine Learning Engineering

Production ML Deployment

CI/CD

Microservices

SQL

Spark

Cloud Platforms

Software Engineering Fundamentals

Version Control

Education

BS in Computer Science

MS in Computer Science / related field

Tools

PyTorch

TensorFlow

AWS

GCP

Apache Spark

SQL

Docker

Kubernetes

Job description

About The Role

The role owns the end-to-end design, implementation, and scaling of machine learning systems powering high-throughput enterprise applications.

The team works at the intersection of applied research and platform engineering, building resilient infrastructure that ensures models operate reliably at production scale.

Key Responsibilities

• Architect and deploy production-grade machine learning models using Python, PyTorch, and cloud infrastructure on AWS
• Build robust data ingestion and feature engineering pipelines using Apache Spark and SQL to support continuous model training
• Optimize model serving architectures for low latency and high availability, utilizing containerization tools like Docker and Kubernetes
• Implement comprehensive monitoring systems to track data drift, concept drift, and system performance regressions in real time
• Collaborate with backend engineers and product teams to integrate machine learning capabilities seamlessly into core software services
• Contribute to internal engineering standards by writing clean, well-tested code and participating in rigorous peer code reviews

What We Are Looking For

• 3 to 6 years of professional experience in machine learning engineering or applied software development
• Strong proficiency in Python and hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow
• Demonstrated track record of deploying and maintaining ML models in production environments using cloud platforms like AWS or GCP
• Solid foundation in software engineering principles, CI/CD pipelines, and microservices architecture
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field
• Bonus: Experience with LLM orchestration frameworks, vector databases, or large-scale distributed computing systems