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Allegis Group - Austin, TX
posted 2 months ago
Full-time - Mid Level
Hybrid - Austin, TX
10,001+ employees
Administrative and Support Services
About the position
We are seeking a talented and innovative AI / Machine Learning Engineer to join our team. This hybrid role involves designing, building, and deploying advanced machine learning models and AI solutions to solve real-world problems. The engineer will work with large datasets and collaborate with cross-functional teams to deliver impactful results that align with business goals.
Responsibilities
- Develop, test, and optimize machine learning models for classification, regression, clustering, or recommendation tasks.
- Collect, clean, preprocess, and analyze large datasets to create high-quality training datasets.
- Implement machine learning algorithms and neural networks using frameworks like TensorFlow, PyTorch, and scikit-learn.
- Deploy trained models into production environments using APIs, containers (e.g., Docker), or cloud services (AWS, Google Cloud Platform, or Azure).
- Monitor model performance, detect drift, and implement improvements or retraining strategies to ensure models remain accurate over time.
- Collaborate with data management team, applications team, enterprise architects, and product managers to align solutions with business needs.
- Create thorough documentation for models, processes, and experiments to ensure reproducibility and scalability.
- Develop automated pipelines for continuous integration, delivery, and model retraining (CI/CD).
- Ensure AI models comply with industry regulations, address biases, and adhere to ethical standards.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Engineering, or a related field.
- Proficiency in Python, R, or similar programming languages.
- Experience with TensorFlow, PyTorch, Keras, or scikit-learn.
- Strong understanding of SQL, NoSQL, and big data tools (e.g., Spark, Hadoop).
- Familiarity with AWS, Google Cloud, or Microsoft Azure for deploying ML models.
- Expertise in using metrics like accuracy, precision, recall, RMSE, or AUC-ROC for performance evaluation.
- Experience with GitHub, GitLab, or other version control tools.
- Strong analytical and problem-solving skills.
- Ability to explain complex AI/ML concepts to non-technical stakeholders.
