Associate AI Engineer

The Associate AI Engineer will be responsible for designing, developing, and implementing AI systems and data pipelines that enhance and automate university operations across multiple departments. Transforms manual processes into AI-driven solutions, focusing on building robust data pipelines, creating efficient machine learning models, and integrating AI capabilities into existing systems to improve efficiency, accuracy, and service quality while reducing operational costs, utilizing expertise in machine learning, natural language processing, data engineering, and AI system integration with existing enterprise infrastructure.

• Knowledge and skills required for this position are normally obtained through a Bachelor's degree in Linguistics, Computational Linguistics, Computer Science, or related field; with four to six years of experience working with AI or machine learning , with demonstrated success in enterprise applications. Experience in higher education or similar complex organizational environments preferred.
• Other necessary skills: LLM Expertise:
  Deep understanding of large language model capabilities, limitations, and optimal interaction patterns, with demonstrated experience designing effective prompts for enterprise applications.
• AI/ML Development Expertise:
  Strong proficiency in developing and deploying machine learning models and AI systems in production environments, with deep knowledge of contemporary AI frameworks, tools, and best practices.
• Software Engineering:
  Excellent software development skills with proficiency in Python, Tensor Flow/PyTorch, and experience with containerized deployments and MLOps practices.
• Data Pipeline Engineering:
  Extensive experience with end-to-end data pipelines, data warehousing solutions , processing frameworks, and container technologies, with proficiency in Python, SQL, and version control/CI/CD practices.
• Machine Learning Engineering:
  Demonstrated experience in the full ML lifecycle including data preparation, feature engineering, model training, validation, deployment, and monitoring in production.
• Natural Language Processing:
  Advanced knowledge of NLP techniques and large language models (LLMs), including prompt engineering, context management, and implementation strategies for enterprise applications.
• Cloud Computing:
  Experience deploying and scaling AI systems in cloud environments, with knowledge of cloud-native AI services.
• Solution Architecture:
  Ability to design scalable, secure, and efficient AI system architectures that meet enterprise requirements and performance standards.
• System Integration:
  Ability to integrate AI solutions with existing enterprise systems, APIs, databases, and authentication services to create cohesive user experiences.
• Performance Optimization:
  Experience optimizing AI models for both accuracy and computational efficiency in resource-constrained environments.
• Security Awareness:
  Knowledge of security best practices for AI systems, including data protection, model security, and prevention of adversarial attacks.
• Data Science:
  Strong understanding of data structures, algorithms, statistical analysis, and data visualization techniques relevant to AI applications.
• AI Ethics and Governance:
  Understanding of ethical considerations in AI development, including bias mitigation, fairness, transparency, and compliance with relevant regulations.

• AI System Design and Development Design, develop, and implement AI solutions to automate and enhance university operations, including service desk automation, administrative task processing, and QA testing systems. Create robust, scalable architectures that integrate with existing university…