Machine Learning Engineer

Evlo AI; Raleigh (NC); On-site; USD 130,000 - 180,000; Full time

Job summary

Evlo AI is seeking an ML Engineer to own the design, training, and production deployment of models powering core features, including tabular ML, NLP, and emerging LLM workflows. You will work in a small, senior ML team to optimize performance, latency, and reliability of production systems, build end-to-end data and feature pipelines, monitor drift, and implement automated retraining.

Collaboration with data engineering and product teams will be essential as you champion MLOps and scalable

Qualifications

• Production deployments of ML models across systems.
• Strong Python and experience with PyTorch, TensorFlow, or scikit-learn.
• Experience with cloud ML platforms and MLOps tooling (MLflow, model registries, CI/CD for ML).
• Solid ML fundamentals: feature engineering, evaluation design, regularization, drift handling.
• Experience with containerized deployment (Docker, Kubernetes) and serving systems such as TorchServe, Triton, or FastAPI.
• MS or BS in Computer Science, Statistics, Engineering, or equivalent practical experience.

Responsibilities

• Design, train, and evaluate ML models for production workloads.
• Build and maintain feature pipelines in Python and SQL, ensuring consistency between training and online serving paths.
• Deploy and version models using Docker, Kubernetes, and cloud ML platforms, with automated CI/CD for model releases.
• Instrument models for monitoring: track drift, latency, and business metrics with alerting and automated retraining triggers.
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow, and modern data stack tooling.
• Introduce and champion LLM-based capabilities where they add value - RAG systems, embeddings, fine-tuning - with rigorous evaluation.
• Contribute to code reviews, design docs, and the team's ML engineering standards.

Skills

Python

PyTorch

TensorFlow

scikit-learn

MLflow

Docker

Kubernetes

TorchServe

Triton

FastAPI

Education

MS in Computer Science

BS in Computer Science/Statistics/Engineering

Tools

MLflow

Model registries

Docker

Kubernetes

TorchServe

Triton

FastAPI

Job description

About The Role

The role owns the design, training, and production deployment of machine learning systems that directly power core product features - spanning tabular ML, NLP, and increasingly LLM-based workflows.

Working in a small, senior team of ML engineers and data scientists, this person will tackle problems where model performance, inference latency, and reliability all matter - and ship systems that run in production, not just in notebooks.

Key Responsibilities

• Design, train, and evaluate ML models (classification, ranking, NLP, and recommendation use cases) for production workloads
• Build and maintain feature pipelines in Python and SQL, ensuring consistency between training and online serving paths
• Deploy and version models using Docker, Kubernetes, and cloud ML platforms (SageMaker, Vertex AI, or Azure ML), with automated CI/CD for model releases
• Instrument models for monitoring: track drift, latency, and business metrics with alerting and automated retraining triggers
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow, and modern data stack tooling
• Introduce and champion LLM-based capabilities where they add value - RAG systems, embeddings, fine-tuning - with rigorous evaluation
• Contribute to code reviews, design docs, and the team's ML engineering standards

What We Are Looking For

• 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple production model deployments
• Expert-level Python and hands-on depth in at least one major framework: PyTorch, TensorFlow, or scikit-learn
• Production experience with cloud ML platforms and MLOps tooling (MLflow, model registries, CI/CD for ML)
• Strong ML fundamentals: feature engineering, evaluation design, regularization, and handling class imbalance and drift
• Experience with containerized deployment (Docker, Kubernetes) and serving systems such as TorchServe, Triton, or FastAPI
• MS or BS in Computer Science, Statistics, Engineering, or equivalent practical experience