AI Engineer 360 Huntington Avenue Boston MA 02115 – hybrid (3 days onsite) 12+ months Contract with Extn Pay Rate- $96/hr on W2 Job Description The implementation of AI systems across Client presents a significant opportunity to enhance teaching and learning, research, and administrative operations while freeing operational time and cost for reinvestment. To realize this value, specialized expertise is required that does not currently exist within the central IT organization. The proposed AI Engineer, AI Operations Specialist, and AI Prompt Engineer roles represent critical first capabilities needed to further design, implement, and maintain enterprise-grade AI systems and their supporting data pipelines university-wide. These specialized positions will enable automation of routine functions across the institution and power the wide and expanding array of AI-based institutional initiatives, while creating a stronger foundation for future AI adoption throughout the global campus network. The specialized knowledge required—spanning data pipeline engineering, machine learning implementation, and AI system operations—is distinct from traditional IT roles. By establishing these positions now, Northeastern will strengthen its position at the forefront of AI adoption in higher education, enhancing student experiences, accelerating research capabilities, and improving administrative efficiency across all university functions. These roles represent both a strategic investment in the university's future and a fiscally responsible approach to institutional advancement through technological innovation. 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, TensorFlow/PyTorch, and experience with containerized deployments and MLOps practices. • Data Pipeline Engineering: Extensive experience with end-to-end data pipelines using tools like Apache Airflow, Prefect, cloud platforms (AWS, Azure, GCP), data warehousing solutions (Snowflake, Redshift), processing frameworks (Spark, Kafka), and container technologies (Docker, Kubernetes), 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 (AWS, Azure, or GCP), 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. • Problem-Solving: Exceptional problem-solving skills to troubleshoot complex AI system issues and optimize performance in real-world applications. • Project Management: Ability to manage AI development projects, prioritize tasks, and deliver solutions on schedule and within scope. • Cross-functional Collaboration: Demonstrated ability to work effectively with diverse stakeholders, translating business requirements into technical specifications and explaining technical concepts to non-technical audiences. • Communication Skills: Excellent verbal and written communication skills to document systems, present findings, and collaborate with cross-functional teams. • Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field; Master's degree preferred. • Minimum of 5 years of experience in AI/ML engineering roles, with at least 2 years working with production AI systems in enterprise environments. • Experience with AI system implementation in higher education or similar complex organizational settings preferred