Production ML Engineer: Scalable Systems

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

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.