AI/ML Engineer, Data Science & Model Deployment
Overview
Design and deploy intelligent systems that learn from data to automate tasks and improve decision-making at scale. This role combines data science research with production-grade software engineering. You will work alongside cross-functional teams in Irving, TX, owning models from proof-of-concept to deployment. Python and TensorFlow are central to daily work. The role offers full-time employment with a focus on real-world impact.
What You'll Do7
- 1Design, develop, and train machine learning and deep learning models using Python and frameworks like TensorFlow, Keras, or PyTorch.
- 2Own data preprocessing pipelines to clean, transform, and feature-engineer datasets for model training.
- 3Deploy trained models into production environments using Docker and Kubernetes.
- 4Drive model evaluation and tuning to meet accuracy and performance targets.
- 5Ship end-to-end ML solutions that automate tasks and improve business outcomes.
- 6Collaborate with data engineers to ensure data quality and availability for Spark-based processing.
- 7Scale model training and inference using AWS or Azure cloud services.
Requirements7
- 15+ years of experience in machine learning or data science roles.
- 2Strong proficiency in Python and its data science ecosystem (pandas, NumPy, scikit-learn).
- 3Hands-on experience with TensorFlow, Keras, or PyTorch for model development.
- 4Experience with SQL and data preprocessing for large datasets.
- 5Familiarity with Docker and Kubernetes for model deployment.
- 6Knowledge of AWS or Azure cloud platforms for ML workloads.
- 7Experience with Spark for distributed data processing.
Salary Insight
Salary not disclosed in listing
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