Production ML Engineer: Build & Deploy AI with Impact

Evlo AI; Denver (CO); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI is seeking a Machine Learning Engineer to design, build, and deploy production ML systems. You will own models from data pipeline through serving, using PyTorch and modern MLOps tooling on cloud infrastructure.

You will work with data engineers, product managers, and backend teams to ship end-to-end ML features, implement monitoring and alerting, and optimize inference performance while meeting latency and cost targets.

Qualifications

• 3–6 years of experience in machine learning engineering with production deployments.
• Expert-level Python and hands-on depth in PyTorch, TensorFlow, or scikit-learn.
• Experience deploying and operating models in production on AWS, GCP, or Azure, including CI/CD for ML systems.
• Strong ML fundamentals: feature engineering, evaluation metrics, regularization, and diagnosing training/serving skew.
• Experience with orchestration and infrastructure tooling: Docker, Kubernetes, Airflow, Terraform, or equivalents.
• Bachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent practical experience)
• Bonus: LLM/GenAI experience, Spark at scale, a published open-source contribution, or a relevant MS/PhD

Responsibilities

• Design, train, and evaluate ML models (classification, ranking, forecasting, NLP) for production use cases with strict accuracy and latency requirements
• Build and maintain data and feature pipelines using Python, PySpark, and tools like Airflow or dbt, ensuring consistency between training and serving
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML), including A/B testing and rollback strategies
• Instrument models in production for drift detection, performance monitoring, and automated alerting; own incident response for model quality issues
• Optimize inference performance — batching, quantization, caching, and model distillation — to meet cost and latency targets
• Collaborate with data engineers, product managers, and backend teams to scope problems, define success metrics, and ship end-to-end ML features
• Contribute to team standards: code reviews, experiment tracking (MLflow, W&B), reproducible training pipelines, and documented model cards

Skills

Python

PyTorch

TensorFlow

scikit-learn

Education

Bachelor's degree in Computer Science, Engineering, Statistics, or related field

Tools

Docker

Kubernetes

Airflow

Terraform

MLflow

Weights & Biases

Job description

Evlo AI is seeking a Machine Learning Engineer to design, build, and deploy production ML systems. You will own models from data pipeline through serving, using PyTorch and modern MLOps tooling on cloud infrastructure.

You will work with data engineers, product managers, and backend teams to ship end-to-end ML features, implement monitoring and alerting, and optimize inference performance while meeting latency and cost targets.