Position Overview
TEGNA is looking for a Senior AI Engineer to join the dynamic engineering team. We are looking for a Senior AI/ML Engineer to design, build, and deploy machine learning solutions that power data-driven products and intelligent systems. The role involves developing scalable ML models, building automated ML pipelines, and integrating models into production systems operating at scale. The engineer will collaborate with data scientists, software engineers, and platform teams to develop reliable and production-ready AI solutions.
What You’ll Do
- Design, develop, and deploy machine learning models for predictive analytics and optimization use cases.
- Build scalable model training and deployment pipelines using Amazon SageMaker.
- Develop end-to-end ML workflows including data preparation, feature engineering, model training, evaluation, and deployment.
- Automate model lifecycle management using Amazon SageMake Pipelines.
- Integrate machine learning models with backend services and distributed applications.
- Monitor model performance, identify model drift, and retrain models as needed.
- Work closely with engineering teams to ensure ML systems are scalable, reliable, and maintainable.
- Mentor engineers and contribute to architectural decisions related to AI/ML systems.
What you bring
- Bachelor’s or Master’s degree in computer science, Machine Learning, Data Science, or related field.
- 8+ years of overall software engineering experience
- 3+ years of experience building machine learning models and AI systems
- Strong programming skills in Python
- Strong understanding of Machine learning fundamentals including feature engineering, model evaluation and model deployment
- Experience working with large-scale data systems and cloud-based architecture.
Experience with ML frameworks such as:
- Scikit-learn
- XGBoost
- TensorFlow
- LightGBM
Preferred Qualification
- Experience building production ML pipelines and automated training
- Familiarity with ML lifecycle management tools.
- Experience with distributed data processing or large-scale data
- Knowledge of modern AI techniques such as deep learning, reinforcement learning, or optimization models.
- Experience building monitoring and observability for ML systems.