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.