Senior Machine Learning Engineer

About the Opportunity

JOB SUMMARY
The Sr Machine Learning (ML) Engineer applies expertise in deploying and scaling AI pipelines across at least one major cloud platform (AWS, GCP, or Azure). The Engineer will collaborate with our Data Scientists to architect, deploy, and maintain production-grade AI solutions. As part of the AI Solutions Hub, the Engineer will architect, engineer, and deploy AI pipelines that push technological boundaries for our clients. The Engineer will tackle complex challenges at the intersection of Large Language Models, Computer Vision, and Predictive Analytics while ensuring production reliability and scalability. The Engineer combines technical excellence with strong collaboration skills to help our clients realize transformative AI projects.

MINIMUM QUALFICIATIONS

• Expert knowledge of at least one major cloud platform (AWS, GCP, or Azure)
• Strong programming skills in Python and infrastructure-as-code tools
• Proficient with containerization (Docker) and orchestration (Kubernetes)
• Knowledge and skills required for this job are normally obtained through a bachelor's degree at at least 3+ years of experience in software engineering with a focus on cloud infrastructure plus 1 more years of hands-on experience deploying ML models to production

JOB DUTIES
1) Model Development & Deployment

• Design and implement scalable model serving architectures for both GenAI (LLMs, diffusion models) and traditional ML models
• Build and maintain real-time and batch inference pipelines with high availability and fault tolerance
• Optimize AI workloads for performance, cost-efficiency, and low-latency inference
• Develop distributed model training and inference architectures leveraging GPU-based compute resources
• Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native services
   

2) MLOps Pipeline Development

• Architect end-to-end MLOps pipelines covering training, validation, deployment, and monitoring
• Design and implement model validation and monitoring systems with alerts
• Automate data pipelines for feature engineering, model retraining, and data versioning using cloud data services (e.g., Redshift, BigQuery, Synapse)
• Implement monitoring for model drift, data drift, and service reliability
   

3) DevOps & Automation

• Implement CI/CD pipelines for ML model deployment using GitHub Actions, Jenkins, or equivalent
• Develop infrastructure-as-code templates for reproducible environment setup
• Design and build scalable cloud infrastructure using compute, storage, and database services (e.g., EC2, Cloud Storage, Cosmos DB)
• Ensure high availability, auto-scaling, and fault tolerance of AI services in production
   

4) Security & Compliance

• Design and maintain IAM roles and permissions for ML workflows
• Ensure compliance with data privacy requirements in model serving
• Implement encryption and key management for model artifacts and sensitive data
• Set up secure access controls using cloud-native security services (e.g., KMS, Cloud KMS, Key Vault)

Position Type

Research

Additional Information

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.  

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:

113S

Expected Hiring Range:

$113,865.00 - $165,105.00

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.