Machine Learning Engineer

Evlo AI; Raleigh (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

Tech Ladies, Inc. is seeking an ML Engineer to own design, training, and production deployment of ML systems powering core product features, including tabular ML, NLP, and emerging LLM-based workflows.

In a small, senior team, you will tackle issues of model performance, latency, and reliability, shipping systems that run in production rather than notebooks. Expect collaboration with data engineering and an emphasis on rigorous evaluation and monitoring.

Qualifications

• 3–7 years of experience with multiple production model deployments.
• Expert-level Python and hands-on depth in 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, and handling drift.
• Experience with Docker/Kubernetes and serving systems such as TorchServe, Triton, or FastAPI.

Responsibilities

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

Skills

Python

Deep learning frameworks

Cloud ML platforms

MLOps tooling

ML fundamentals

Containerized deployment

CS degree relevance

Education

MS/BS in CS or related

Tools

Docker

Kubernetes

TorchServe/Triton/FastAPI

MLflow

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