Machine Learning Engineer

Evlo AI; Phoenix (AZ); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI is seeking a seasoned Machine Learning Engineer to own end-to-end ML systems—from research and prototyping to low-latency production deployments. You will build scalable pipelines and model endpoints, and collaborate with backend teams to ensure robust, production-grade solutions.

The role requires strong Python, PyTorch or TensorFlow expertise, and hands-on experience with AWS SageMaker, Docker, Kubernetes, and MLflow.

Qualifications

• 3–6 years of professional software engineering experience, with at least 3 years in production ML engineering.
• Expert Python, deep familiarity with PyTorch or TensorFlow.
• Experience with cloud infrastructure and MLOps tools like AWS SageMaker, MLflow, Docker, and Kubernetes.
• Understanding of distributed systems, data structures, and algorithms.
• BS or MS in CS, ML, or related field.
• Bonus: experience with LLMs, vector databases, or real-time streaming with Kafka.

Responsibilities

• Design, train, and validate ML models for production use cases.
• Build high-throughput data ingestion and feature pipelines with Spark.
• Deploy, monitor, and scale models on AWS using SageMaker, Docker, and Kubernetes.
• Implement monitoring for data drift and prediction latency in production.
• Collaborate with backend engineers to expose model endpoints in microservices.
• Conduct code reviews, tests, and establish ML engineering best practices.

Skills

Python

PyTorch

TensorFlow

Docker

Kubernetes

MLOps

AWS SageMaker

Distributed systems

Algorithms

Education

Bachelor's degree in CS/ML

Tools

Apache Spark

MLflow

Kafka

Job description

About The Role

The role owns the full lifecycle of machine learning systems, from experimental research and prototyping to reliable, low-latency production deployments.

The engineering team builds robust ML infrastructure and production-grade models that scale to support core business operations and millions of daily queries.

Key Responsibilities

• Design, train, and validate machine learning models using PyTorch and Python for complex production use cases
• Build high-throughput data ingestion and feature engineering pipelines using Apache Spark and distributed computing frameworks
• Deploy, monitor, and scale models on AWS using SageMaker, Docker, and Kubernetes with strict latency and reliability guarantees
• Implement comprehensive monitoring systems to track data drift, concept drift, and prediction latency in production
• Collaborate with backend engineers to integrate model endpoints into scalable microservices and APIs
• Conduct code reviews, write unit and integration tests, and establish best practices for machine learning

What We Are Looking For

• 3-6 years of professional experience in software engineering, with at least 3 years focused on machine learning engineering in production
• Expert proficiency in Python and deep familiarity with PyTorch or TensorFlow
• Hands-on experience with cloud infrastructure and MLOps tools such as AWS SageMaker, MLflow, Docker, and Kubernetes
• Solid understanding of distributed systems, data structures, and algorithms
• BS or MS in Computer Science, Machine Learning, or a related quantitative field
• Bonus: Experience with large language models, vector databases, or real-time streaming architectures using Kafka