AI/ML (Artificial Intelligence/Machine Learning) Engineer

Job Description:

We are seeking a talented and innovative AI / Machine Learning Engineer to join our team. As part of the AI Technical Team, you will design, build, and deploy advanced machine learning models and AI solutions to solve real-world problems. You will work with large datasets, AI/ML frameworks, and cross-functional teams to deliver impactful results that align with our business goals.

If you are passionate about machine learning, artificial intelligence, and creating high-performance models, we’d love to hear from you.

Key Responsibilities

Model Development and Design: Develop, test, and optimize machine learning models for classification, regression, clustering, or recommendation tasks.
Data Preparation: work alongside the Enterprise Data Management Team to collect, clean, preprocess, and analyze large datasets to create high-quality training datasets.
Algorithm Implementation: Implement machine learning algorithms and neural networks using frameworks like TensorFlow, PyTorch, and scikit-learn.
Deployment and Integration: Deploy trained models into production environments using APIs, containers (e.g., Docker), or cloud services (AWS, GCP, or Azure).
Performance Monitoring: Monitor model performance, detect drift, and implement improvements or retraining strategies to ensure models remain accurate over time.
Collaboration: Work closely with our data management team, applications team, enterprise architects, and product managers to align solutions with business needs.
Documentation: Create thorough documentation for models, processes, and experiments to ensure reproducibility and scalability.
MLOps Practices: Develop automated pipelines for continuous integration, delivery, and model retraining (CI/CD).
Ethics and Compliance: Ensure AI models comply with industry regulations, address biases, and adhere to ethical standards.

Responsibilities include (but are not limited to):
Model Development and Design: Develop, test, and optimize machine learning models for classification, regression, clustering, or recommendation tasks.
Data Preparation: work alongside the Enterprise Data Management Team to collect, clean, preprocess, and analyze large datasets to create high-quality training datasets.
Algorithm Implementation: Implement machine learning algorithms and neural networks using frameworks like TensorFlow, PyTorch, and scikit-learn.