Tebra - Senior Machine Learning Engineer

• 5+ years of professional software development experience including system design, large-scale services, and production-grade infrastructure. • 5+ years • 3+ years of hands-on experience in machine learning engineering or applied AI, with a strong record of deploying and maintaining models in production. • 3+ years • machine learning engineering or applied AI • Technical subject matter expertise in 3+ general areas of software development (e.g., server, database, security, etc) including machine learning infrastructure. • Technical subject matter expertise • Demonstrated ability to deliver significant, measurable real-world impact through applied ML. • measurable real-world impact through applied ML • Proven ability to design and write modular, performant, and easy to read software that solves complex business problems. • design and write modular, performant, and easy to read • Proficiency in Python, TensorFlow/PyTorch, and scikit-learn. • Python, TensorFlow/PyTorch, and scikit-learn • 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 Large Language Models (LLMs) or generative models for structured business processes. • Experience building or fine-tuning Large Language Models (LLMs) • or generative model • Experience with retrieval-augmented pipelines or feedback-driven model retraining. • retrieval-augmented pipelines or feedback-driven model retraining • Excellent technical communication and a product mindset—comfortable driving initiatives from concept to delivery. • product mindset • Background in healthcare software operations, or financial automation. • healthcare software operations, or financial automation • Contributions to open-source ML infrastructure projects. • open-source ML infrastructure • Published research or conference papers in machine learning, natural language processing, or applied AI • Published research or conference papers • 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 • (For Recruiter use only) • #LI-SS1 #LI-Remote • We are dedicated to attracting and retaining top talent with competitive and fair compensation. For this position, this range reflects our Zone 1 (National Average) pay band. Your specific compensation is thoughtfully determined by your experience, qualifications, the specific requirements of the role, and your Geo Zone. Our geo-zone system ensures your pay is competitive for your location, recognizing varying costs of labor across regions. • Our four geo zones are designed to reflect this:Zone 1: National AverageZone 2: Moderately Higher Cost RegionsZone 3: High-Cost RegionsZone 4: Lower-Cost Regions