Machine Learning Engineer

Evlo AI; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

Evlo AI in Chicago, IL is seeking an experienced ML Engineer to own end-to-end lifecycle of ML systems, translating research into robust production services.

You will collaborate with data engineers and product stakeholders to build high-throughput models where inference latency, accuracy, and resilience are critical. 3–6 years in software engineering with strong ML focus is required.

Qualifications

• 3 to 6 years of professional software engineering experience, with at least 3 years in ML engineering.
• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Proven track record deploying and maintaining ML models in production using AWS, GCP, or Azure.
• Solid understanding of distributed data processing tools like Spark or Ray, and vector databases like Pinecone or Milvus.
• BS or MS in Computer Science, Machine Learning, Statistics, or related quantitative field.
• Bonus: Experience fine-tuning large language models or building agentic RAG architectures.

Responsibilities

• Architect and deploy scalable ML pipelines using Python, PyTorch, and MLOps tooling.
• Develop high-performance inference APIs using FastAPI or gRPC with strict latency SLAs.
• Optimize model architectures for production via quantization, pruning, and distributed training.
• Build automated monitoring pipelines to track data drift and system health metrics.
• Collaborate with infrastructure teams to deploy containers using Docker, Kubernetes, and Terraform.
• Conduct code reviews and contribute to ML reproducibility standards.

Skills

Python

PyTorch

TensorFlow

MLOps

Model deployment

APIs

Docker

Kubernetes

Terraform

Cloud platforms

Distributed training

Education

BS or MS in Computer Science or related field

Tools

Docker

Kubernetes

Terraform

FastAPI

gRPC

Spark

Ray

Pinecone

Milvus

AWS

GCP

Azure

Job description

About The Role

The role owns the end-to-end lifecycle of machine learning and deep learning systems, translating cutting-edge AI research into robust, scalable production services.

The team collaborates closely with data engineers and product stakeholders to build high-throughput models where inference latency, accuracy, and system resilience are paramount.

Key Responsibilities

• Architect and deploy scalable machine learning pipelines using Python, PyTorch, and modern MLOps tooling
• Develop high-performance inference APIs using FastAPI or gRPC to serve models with strict latency SLAs
• Optimize model architectures for production via quantization, pruning, and distributed training techniques
• Build automated monitoring pipelines to track data drift, concept drift, and system health metrics
• Collaborate with infrastructure teams to manage containerized deployments using Docker, Kubernetes, and Terraform
• Conduct thorough code reviews and contribute to internal standards for ML reproducibility and experimentation

What We Are Looking For

• 3 to 6 years of professional experience in software engineering, with at least 3 years focused specifically on machine learning engineering
• Strong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlow
• Demonstrated track record of deploying and maintaining ML models in production environments using AWS, GCP, or Azure
• Solid understanding of distributed data processing tools like Spark or Ray, and vector databases like Pinecone or Milvus
• BS or MS in Computer Science, Machine Learning, Statistics, or a related quantitative field
• Bonus: Experience fine-tuning large language models or building agentic RAG architectures