Infrastructure Management
- Manage and maintain complex cloud environments for AI workloads.
- Ensure secure, stable connectivity between internal systems and AI platforms.
- Perform ongoing system maintenance and monitoring to guarantee optimal AI model performance.
- Implement and maintain security protocols for AI data handling and model deployment.
Rapid Prototyping & Technical Validation
- Conduct technical due diligence for new AI technologies, frameworks, and vendor solutions.
- Build proof-of-concepts to validate AI model feasibility and performance.
- Perform stress-testing of AI vendor APIs, model endpoints, and third-party integrations.
- Document technical findings and provide recommendations for AI technology adoption.
Pipeline Development & AI Infrastructure
- Develop and maintain automated data pipelines for AI model training and inference.
- Write robust code to ensure reliable data movement from source systems to AI models.
- Implement MLOps practices for model deployment, monitoring, and maintenance.
- Build and maintain AI model serving infrastructure and API endpoints.
Must-Have Requirements
- Bachelor's degree in Computer Science, Data Science, AI/Machine Learning, or related technical field.
- 3+ years of experience in AI/ML engineering or related technical roles.
- Strong programming skills in Python, with experience in AI/ML libraries (TensorFlow, PyTorch, scikit-learn).
- Experience with cloud infrastructure management (AWS, Azure, or GCP).
- Knowledge of MLOps practices, model deployment, and monitoring.
- Experience with API development and integration for AI services.
- Understanding of data pipeline tools and ETL processes for AI applications.
