Machine Learning Engineer

Evlo AI; Miami (FL); On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

None

Job summary

Evlo AI is seeking an ML Engineer to own end-to-end development of production ML systems, from training to deployment and monitoring, powering core product features at scale. The role blends research and engineering, requiring models to be accurate, fast, cost-efficient, and reliable, with ownership over what ships to production.

You'll work across ML, DL, and LLM-powered features in a growth-stage environment.

Qualifications

• 3–6 years of experience in ML engineering or applied ML, with multiple models shipped to production.
• Strong Python engineering skills with hands-on depth in PyTorch or TensorFlow.
• Experience deploying and operating models on cloud providers with SLAs.
• Fundamentals in evaluation, regularization, and handling class imbalance.
• Experience with experiment tracking and CI/CD for ML workflows.
• Degree in CS, Statistics, Engineering, or equivalent practical experience.

Responsibilities

• Design, train, and evaluate ML models for production use cases across ranking, classification, NLP, and generative AI.
• Build and maintain training/feature pipelines in Python, PySpark, and Airflow with data versioning.
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms, managing latency and cost.
• Implement LLM-based features including RAG, prompts, and evaluation harnesses.
• Establish monitoring for data drift and serving anomalies with automated alerts.
• Collaborate with data engineers, PMs, and scientists to translate problems into ML solutions.
• Contribute to ML platform improvements: experiment tracking, CI/CD, and shared tooling.

Skills

Python engineering

PyTorch/TensorFlow

ML platform CI/CD

Experiment tracking

Data versioning

Cloud ML deployment

Education

Bachelor's or Master's in CS/Statistics/Engineering

Tools

Docker

Kubernetes

SageMaker

Vertex AI

Airflow

PySpark

MLflow/Weights & Biases

Job description

About The Role

The role owns end-to-end development of production ML systems: training, deployment, and monitoring of models that power core product features at scale. Work spans classical ML, deep learning, and LLM-powered features, with direct ownership of what ships to production.

The team operates at the intersection of research and engineering — models here must be accurate, fast, cost-efficient, and reliable. This role sits in a well-funded, growth-stage environment where ML infrastructure decisions directly shape product outcomes.

Key Responsibilities

• Design, train, and evaluate ML models for production use cases spanning ranking, classification, NLP, and generative AI features
• Build and maintain training and feature pipelines in Python, PySpark, and Airflow with clear data versioning and reproducibility standards
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (SageMaker, Vertex AI, or equivalent), owning latency and cost targets
• Implement LLM-based features including RAG pipelines, prompt orchestration, and evaluation harnesses where applicable
• Establish monitoring for data drift, model degradation, and serving anomalies with automated alerting and retraining triggers
• Collaborate with data engineers, product managers, and applied scientists to translate business problems into well-scoped ML solutions
• Contribute to ML platform improvements: experiment tracking, CI/CD for models, and shared tooling

What We Are Looking For

• 3–6 years of experience in machine learning engineering or applied ML, with multiple models shipped to production
• Strong Python engineering skills plus hands-on depth in PyTorch or TensorFlow
• Experience deploying and operating models on AWS, GCP, or Azure with real production SLAs
• Solid ML fundamentals: evaluation methodology, regularization, handling class imbalance, and offline/online metric alignment
• Experience with MLflow, Weights & Biases, or equivalent experiment tracking, and CI/CD for ML workflows
• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or equivalent practical experience
• Bonus: experience fine-tuning or serving open-source LLMs, vector databases, Kubernetes-based model serving, or a published paper or open-source ML contribution