Associate AI Engineer

Northeastern University•Boston, MA

1d•$87,785 - $123,999

About The Position

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.

Requirements

Bachelor's degree in Linguistics, Computational Linguistics, Computer Science, or related field
Four to six years of experience working with AI or machine learning, with demonstrated success in enterprise applications.
Deep understanding of large language model capabilities, limitations, and optimal interaction patterns, with demonstrated experience designing effective prompts for enterprise applications.
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.
Excellent software development skills with proficiency in Python, TensorFlow/PyTorch, and experience with containerized deployments and MLOps practices.
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.
Demonstrated experience in the full ML lifecycle including data preparation, feature engineering, model training, validation, deployment, and monitoring in production.
Advanced knowledge of NLP techniques and large language models (LLMs), including prompt engineering, context management, and implementation strategies for enterprise applications.
Experience deploying and scaling AI systems in cloud environments, with knowledge of cloud-native AI services.
Ability to design scalable, secure, and efficient AI system architectures that meet enterprise requirements and performance standards.
Ability to integrate AI solutions with existing enterprise systems, APIs, databases, and authentication services to create cohesive user experiences.
Experience optimizing AI models for both accuracy and computational efficiency in resource-constrained environments.
Knowledge of security best practices for AI systems, including data protection, model security, and prevention of adversarial attacks.
Strong understanding of data structures, algorithms, statistical analysis, and data visualization techniques relevant to AI applications.
Understanding of ethical considerations in AI development, including bias mitigation, fairness, transparency, and compliance with relevant regulations.

Nice To Haves

Experience in higher education or similar complex organizational environments preferred.

Responsibilities

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 systems and accommodate future growth.
Design and implement end-to-end data pipelines that efficiently collect, process, and prepare data for AI systems. Build robust ETL processes using tools like Apache Airflow, cloud services, and data warehousing solutions to ensure reliable data flow between source systems and AI applications. Implement data quality checks, monitoring, and governance practices throughout the pipeline.
Develop and fine-tune machine learning models for specific university use cases, including customizing large language models through prompt engineering, transfer learning, and domain adaptation. Create efficient training pipelines and establish systematic evaluation protocols.
Integrate AI systems with existing university infrastructure, including identity management, knowledge bases, ticketing systems, and communication platforms. Deploy models to production environments following established MLOPs practices and ensuring appropriate monitoring.
Monitor AI system and data pipeline performance, detect and address drift or degradation, optimize resource utilization, and continuously improve model accuracy and efficiency based on real-world usage patterns and feedback.