Tebra - Staff Machine Learning Engineer

• 8+ years of professional software engineering experience, including system design, large-scale services, and production-grade infrastructure. • 8+ years of professional software engineering experience • 5+ years of hands-on experience in machine learning engineering or applied AI, with a strong record of deploying and maintaining models in production. • machine learning engineering or applied AI • Demonstrated ability to deliver significant, measurable real-world impact through applied ML — improving efficiency, automation, or business outcomes. • Demonstrated • deliver significant, measurable real-world impact • Proficiency in Python, TensorFlow/PyTorch, and scikit-learn. • Python, TensorFlow/PyTorch • scikit-learn • Hands-on experience with data analysis, feature engineering, and model development on large, complex datasets. • data analysis, feature engineering, and model development • Strong background in MLOps and data infrastructure (e.g., Airflow, Spark, feature stores, MLflow, data versioning). • MLOps and data infrastructure • Proven ability to deploy and maintain ML models in production with CI/CD, monitoring, and alerting. • deploy and maintain ML models • Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization (Kubernetes, Docker). • cloud ML environments • Experience building or fine-tuning LLMs or generative models for structured business processes. • Experience building or fine-tuning LLMs or generative models • Experience with retrieval-augmented pipelines or feedback-driven model retraining. • Experience with retrieval-augmented pipelines or feedback-driven model retraining • Experience working with structured business or healthcare data is a plus. • structured business or healthcare data • Excellent technical communication and a product mindset — comfortable driving initiatives from concept to delivery. • (Nice to have) Background in healthcare software operations, or financial automation. • healthcare software operations, or financial automation • (Nice to have) Contributions to open-source ML infrastructure projects. • open-source ML infrastructure • (Nice to have) Published research or conference papers in machine learning, natural language processing, or applied AI. • Published research or conference papers • (Nice to have) Experience leading AI reliability and observability initiatives — designing monitoring frameworks, drift detection, and alerting systems for multiple production models. • Experience leading AI reliability and observability initiatives • (For Recruiter use only) #LI-SS1 #LI-Remote