Machine Learning Engineer, AI Solutions Hub (AISH)

About the Opportunity

Job Summary

This is a full-time one-year term appointment with the possibility of renewal. The position is in-person at Northeasterns Roux Institute in Portland Maine.

The Machine Learning Engineer (MLE) at the AI Solutions Hub (AISH) the delivery arm of Northeastern Universitys Experiential AI Institute will support the development deployment and maintenance of machine learning systems in collaboration with other AISH employees.

This role is intended for early-career engineers who want to build strong foundations in MLOps cloud-based ML systems and production-oriented AI development. The MLE will contribute to ML pipelines deployment workflows and infrastructure components while learning best practices for scalable reliable and responsible AI systems.

Education & Experience

Bachelors or Masters degree in Computer Science Software Engineering or a closely related field.

0-2 years of experience in software engineering machine learning engineering or applied ML projects.

Experience may include industry work internships co-ops academic research or applied project work.

Exposure to cloud platforms and ML deployment concepts tools and services is required.

Industry experience is preferred.

Knowledge Skills and Abilities

ML Engineering Foundations

Strong programming skills in Python; comfort with software engineering practices including code review testing version control and documentation.

Working knowledge of ML workflows with emphasis on the deployment side: model packaging serving validation and inference rather than research or experimentation.

Familiarity with classical ML techniques and practical exposure to modern AI including deep learning generative AI and large language models.

LLM & Agentic AI Systems

Understanding of how LLMs are deployed served and integrated into applications (e.g. API-based inference model hosting via vLLM TGI or similar serving frameworks).

Familiarity with agentic AI patterns: tool use multi-step reasoning orchestration frameworks (e.g. LangGraph CrewAI or similar) and structured output from LLMs.

Awareness of prompt engineering for production systems: not just conversational prompting but structured prompting for reliable parseable outputs in automated pipelines.

Exposure to AI-assisted development workflows and coding agents as productivity tools.

Cloud Engineering

Experience with at least one cloud platform (AWS Azure or GCP) including core compute storage and networking services.

Familiarity with containerization (Docker) and container orchestration (Kubernetes).

Awareness of infrastructure-as-code concepts (e.g. Terraform CloudFormation) is preferred.

MLOps and Deployment

Build and maintain ML deployment pipelines including model packaging registry management and promotion workflows.

Support batch and real-time inference workflows using appropriate serving frameworks (e.g. FastAPI TorchServe Triton vLLM).

Contribute to model validation A/B testing infrastructure and data and model versioning practices (e.g. DVC MLflow Weights & Biases).

Observability & Production Reliability

Help implement logging monitoring and alerting for deployed ML services (e.g. model latency prediction drift error rates).

Contribute to structured approaches for debugging production model issues understanding the difference between infrastructure failures and model degradation.

Awareness of cost monitoring and resource optimization for GPU and cloud-based ML workloads.

DevOps and Automation

Responsible for CI/CD pipelines for ML applications including automated testing of model artifacts and data validation.

Contribute to reproducible environment setup and configuration management.

Learn and apply best practices for reliability scalability and cost-awareness.

Security and Responsible Engineering

Follow established security and access control practices for ML workflows.

Assist with implementing data privacy and governance requirements.

Responsible for secure handling of credentials model artifacts and sensitive data.

Awareness of LLM-specific security concerns: prompt injection data leakage and output guardrails.

Collaboration and Communication

Ability to clearly communicate technical decisions and tradeoffs to both technical and non-technical audiences with guidance.

Collaborate effectively with cross-functional teams including data scientists engineers project managers and faculty experts.

Willingness to participate in client meetings in a supporting role.

Preferred Experience

Exposure to Kubernetes GPU-based workloads or distributed training/inference concepts.

Familiarity with Git-based workflows and Agile development practices.

Coursework or projects involving NLP computer vision or large language models.

Experience with API design for ML services (REST/gRPC).

Familiarity with vector databases retrieval-augmented generation (RAG) or embedding-based search systems.

Values & Professional Attributes

Ethical and Responsible AI

Awareness of responsible AI principles including fairness transparency and responsible model use.

Willingness to follow established governance documentation and review practices.

Learning and Growth Mindset

Strong interest in machine learning systems cloud engineering and MLOps.

Strong curiosity and motivation to learn new tools techniques and AI methods.

Openness to feedback and mentorship.

Execution and Ownership

Ability to manage assigned tasks meet deadlines and maintain high-quality work.

Proactive attitude and willingness to take increasing responsibility over time.

Position Type

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:

Expected Hiring Range:

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.