Data Scientist (AI Engineering)

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.