Machine Learning Engineer II

About the Role

The engineer will design, implement, optimize, and operationalize AI solutions, handling end‑to‑end development and deployment of machine‑learning models on leading cloud platforms while collaborating with data scientists to productionize models with best coding practices. • Design, develop, and deploy end‑to‑end ML pipelines and AI solutions. • Optimize and fine‑tune deep learning models for performance, accuracy, and efficiency. • Build and manage scalable, secure cloud‑based infrastructure for ML workflows. • Collaborate with data scientists to productionize models using best coding practices. • Monitor, evaluate, and cost‑optimize ML models in production environments. • Develop internal frameworks and libraries to streamline ML development. • Troubleshoot deployment, scalability, and performance issues. • Conduct code reviews and mentor junior ML engineers. • Participate in recruiting and onboarding of new team members. • Stay current with AI industry trends and emerging technologies.

Key Responsibilities

• ▸ml pipelines
• ▸model optimization
• ▸cloud infrastructure
• ▸model production
• ▸monitoring
• ▸code review

What You Bring

Candidates must be proficient in Python, machine‑learning frameworks such as TensorFlow, PyTorch, or scikit‑learn, and cloud platforms like GCP, and should have experience with CI/CD pipelines, containerization, MLOps tools, version control, and computer‑vision libraries, alongside strong communication skills and a bachelor’s degree or equivalent. The role involves frequent sitting and computer work; employees must maintain an ergonomic remote workspace and, if residing near Cincinnati/Northern Kentucky or Atlanta, work in a hybrid on‑site capacity. All team members must reside in the United States and will be processed through E‑Verify. • Proficient in Python with Pandas, NumPy, and Matplotlib for data manipulation and visualization. • Experience with ML frameworks such as TensorFlow, PyTorch, or scikit‑learn. • Hands‑on experience deploying models on GCP and using CI/CD pipelines. • Knowledge of Docker, Kubernetes, and infrastructure‑as‑code tools like Terraform. • Familiarity with MLOps platforms (Kubeflow, TFX) and version control (Git). • Expertise in computer‑vision libraries (OpenCV, scikit‑image) and techniques. • Understanding of generative AI and deep learning for computer vision and NLP. • Strong communication skills and ability to present quantitative analysis. • Agile development mindset and collaborative team orientation. • Bachelor’s degree or equivalent in data science, statistics, computer science, or a related field.

Requirements

• ▸python
• ▸tensorflow
• ▸gcp
• ▸docker
• ▸git
• ▸bachelor’s

Work Environment

Hybrid