Machine Learning and Generative AI Engineer, Digital Transformation

As a Machine Learning and Generative AI Engineer on our team, you will help lead the development of innovative generative AI products that address the needs of our constituents (students, alumni, faculty, researchers, staff, and the community at large). This key technical leadership role requires hands‑on expertise across the full machine learning and AI lifecycle. You will collaborate with data scientists, product managers, and data engineers to operationalize AI models in production, drive core platform capabilities, and apply these in a variety of domains.

You will also develop and deploy novel approaches to optimize existing AI systems and maximize their business value.

• Architect, build, maintain, and improve a suite of GenAI applications and their underlying systems.
• Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA and other parameter‑efficient methods.
• Establish reusable frameworks to streamline model building, deployment and monitoring. Incorporate comprehensive logging, tracing, and alerting mechanisms.
• Build guardrails, compliance rules, and oversight workflows into the GenAI application platform, including approval chains for model updates and staged rollouts for production releases.
• Develop templates, guides, and sandbox environments to support onboarding of new contributors and experimentation with emerging techniques.
• Ensure user‑facing applications built on the GenAI application platform are safe and reliable, enforcing rigorous validation and testing before publishing, and implement a clear peer review process.
• Apply an entrepreneurial mindset to identify opportunities to optimize business processes, improve user experiences, and prototype solutions that demonstrate value.
• Work closely with data scientists and analysts to develop and deploy new product features across web and mobile applications.
• Contribute to and promote sound software engineering practices across the team.
• Mentor and educate team members to adopt best practices in writing and maintaining production‑grade machine learning code.
• Actively contribute to and leverage community best practices and open‑source resources.
• Monitor, debug, and resolve production issues in a timely manner.
• Partner with project managers to ensure projects are delivered on time and within budget.
• Collaborate with Technical Product Managers to track algorithmic performance KPIs and prioritize performance improvements based on effort and impact.
• Build trust and collaboration by being present on‑site and engaging directly with colleagues and various constituents.
• Complete other responsibilities as assigned.

• Minimum of five years’ post‑secondary education or relevant work experience.

• Bachelor’s degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline desired.
• Minimum of two to three years’ software development experience with Python and SQL.
• Minimum of two to three years of experience building and deploying NLP and deep learning model pipelines into a cloud environment.
• Minimum two to three years of experience using PyTorch or Tensorflow, including optimizing code for GPU clusters.
• Experience building advanced GenAI workflows such as retrieval‑augmented generation (RAG), model chaining, dynamic prompting, and parameter‑efficient fine‑tuning (PEFT/SFT) using Lang Chain, Lang Graph, or similar frameworks.
• Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications.
• Experience with embedding models and tuning vector databases (e.g., Qdrant, Pinecone, Weaviate) to improve semantic search and retrieval performance.
• Solid understanding of the theoretical foundations of LLMs, including Transformer architectures and self‑attention mechanisms.
• Experience with relational and No
  
  SQL databases, big data tools (Spark, Kafka), Linux environments, and at least one major cloud provider (AWS, GCP, Azure).
• Familiarity with data pipeline and workflow management tools (e.g., Airflow, Prefect, or Step Functions).
• Strong…