RIT Solutions, Inc.
ML engineer
Location: Richmond, VA
Onsite: once a month, training couple times of week
Interview process: 2 steps, 30 min each on teams (video)
Contract: 6 months to a year then perm
Must have: Machine learning, data bricks, GCP, python, and CI/CD
Required Skills & Experience Strong proficiency in Python for ML development and data manipulation. Experience with Databricks for collaborative development, ML lifecycle management, and lakehouse architecture. Familiarity with GCP AI services (e.g., Vertex AI, BigQuery ML, Cloud Functions). Solid understanding of both supervised and unsupervised ML algorithms, especially clustering and regression. Experience with AI/ML observability tools (e.g., MLflow, Evidently, WhyLabs, or similar) and observability measures (data drift, model drift, concept drift, etc.) Knowledge of CI/CD practices and tools like GitHub Actions. Ability to work with structured and unstructured data from product pricing tools (ex. Oracle's JD Edwards), and other enterprise systems. Preferred Qualifications
Experience in pricing optimization, revenue management, or retail analytics. Familiarity with open-source ML platforms and libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow). Exposure to feature stores, model registries, and automated retraining workflows. Understanding of data governance, security, and compliance in ML systems.
Must have: Machine learning, data bricks, GCP, python, and CI/CD
Required Skills & Experience Strong proficiency in Python for ML development and data manipulation. Experience with Databricks for collaborative development, ML lifecycle management, and lakehouse architecture. Familiarity with GCP AI services (e.g., Vertex AI, BigQuery ML, Cloud Functions). Solid understanding of both supervised and unsupervised ML algorithms, especially clustering and regression. Experience with AI/ML observability tools (e.g., MLflow, Evidently, WhyLabs, or similar) and observability measures (data drift, model drift, concept drift, etc.) Knowledge of CI/CD practices and tools like GitHub Actions. Ability to work with structured and unstructured data from product pricing tools (ex. Oracle's JD Edwards), and other enterprise systems. Preferred Qualifications
Experience in pricing optimization, revenue management, or retail analytics. Familiarity with open-source ML platforms and libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow). Exposure to feature stores, model registries, and automated retraining workflows. Understanding of data governance, security, and compliance in ML systems.