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, 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.
KEY RESPONSIBILITIES & ACCOUNTABILITIES
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 systems and accommodate future growth.
Data Pipeline Development and Management
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.
Machine Learning Implementation and Fine-tuning
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.
System Integration and Deployment
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.
Performance Monitoring and Optimization
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.
Position Type
Information Technology
Additional Information
Northeastern University considers factors such as candidate work
experience, education and skills when extending an offer.
Northeastern has a comprehensive benefits package for benefit
eligible employees. This includes medical, vision, dental, paid
time off, tuition assistance, wellness & life, retirement- as
well as commuting & transportation. Visit
https://hr.northeastern.edu/benefits/ for
more information.
All qualified applicants are encouraged to apply and will receive
consideration for employment without regard to race, religion,
color, national origin, age, sex, sexual orientation, disability
status, or any other characteristic protected by applicable
law.
Compensation Grade/Pay Type:
111S
Expected Hiring Range:
$87,785.00 - $123,998.75
With the pay range(s) shown above, the starting salary will depend
on several factors, which may include your education, experience,
location, knowledge and expertise, and skills as well as a pay
comparison to similarly-situated employees already in the role.
Salary ranges are reviewed regularly and are subject to
change.
To apply, visit https://northeastern.wd1.myworkdayjobs.com/en-US/careers/job/Boston-MA-Main-Campus/Associate-AI-Engineer_R141445
je-6f518573ea844bec8159c477d6dc7e63
