Machine Learning Engineer
evlo aiMiami (FL)
About the role
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 For3–6 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 TensorFlowExperience deploying and operating models on AWS, GCP, or Azure with real production SLAsSolid 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
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