Position Overview
The AI/ML Engineer is a key technical contributor driving AI transformation initiatives. This role focuses on building and deploying intelligent, cloud-native applications—from GenAI-powered systems and retrieval-augmented assistants to data-driven automation workflows.
Working at the intersection of machine learning, cloud engineering, and educational innovation, the engineer translates complex needs into scalable, secure, and maintainable AWS-native AI systems that enhance teaching, learning, and operations across global online programs.
Key Responsibilities
AI Application & Systems Development
Own the design and end-to-end implementation of AI systems combining GenAI, narrow AI, and traditional ML models (e.g., regression, classification).
Implement retrieval-augmented generation (RAG), multi-agent, and protocol-based AI systems (e.g., MCP).
Integrate AI capabilities into production-grade applications using serverless and containerized architectures (AWS Lambda, Fargate, ECS).
Fine-tune and optimize existing models for specific educational and administrative use cases, focusing on performance, latency, and reliability.
Build and maintain data pipelines for model training, evaluation, and monitoring using AWS services such as Glue, S3, Step Functions, and Kinesis.
Cloud & Infrastructure Engineering
Architect and manage scalable AI workloads on AWS, leveraging services such as SageMaker, Bedrock, API Gateway, EventBridge, and IAM-based security.
Build microservices and APIs to integrate AI models into applications and backend systems.
Develop automated CI/CD pipelines ensuring continuous delivery, observability, and monitoring of deployed workloads.
Apply containerization best practices using Docker and manage workloads through AWS Fargate and ECS for scalable, serverless orchestration and reproducibility.
Ensure compliance with Stanford and regulatory standards (FERPA, GDPR) for secure data handling and governance.
Collaboration, Culture & Continuous Improvement
Collaborate closely with cross-functional teams to deliver integrated and impactful AI solutions.
Use Git-based version control and code review best practices as part of a collaborative, agile workflow.
Operate within an agile, iterative development culture, participating in sprints, retrospectives, and planning sessions.
Continuously learn and adapt to emerging AI frameworks, AWS tools, and cloud technologies. Contribute to documentation, internal knowledge sharing, and mentoring as the team scales.
Requirements
Required Qualifications
Education & Certifications
Bachelor's degree in Computer Science, AI/ML, Data Engineering, or a related field (Master's preferred).
AWS certification preferred (Solutions Architect, Developer, or equivalent); Professional-level certification a plus.
Experience
3 years of experience developing and deploying AI/ML-driven applications in production. 2 years of hands-on experience with AWS-based architectures (serverless, microservices, CI/CD, IAM).
Proven ability to design, automate, and maintain data pipelines for model inference, evaluation, and monitoring.
Experience with both GenAI and traditional ML techniques in applied, production settings.
Technical Skills
Languages: Python (required); familiarity with Go, Rust, R, or TypeScript preferred.
AI/ML Frameworks: PyTorch, TensorFlow, LangChain, LlamaIndex, or similar.
Cloud & Infrastructure: AWS SageMaker, Bedrock, Lambda, ECS/Fargate, API Gateway, EventBridge, Glue, S3, Step Functions, IAM, CloudWatch.
Infrastructure as Code: AWS CloudFormation.
DevOps & Tools: Git, Docker, AWS Fargate, ECS, CI/CD (GitHub Actions, CodePipeline).
Data Systems: SQL/NoSQL, vector databases, and AWS-native data services.
Desired Attributes
Strong understanding of data engineering fundamentals and production-quality AI system design.
Passion for applying AI to improve educational outcomes and operational efficiency. Excellent problem-solving, debugging, and communication skills.
Demonstrated ability to learn rapidly, adapt to new technologies, and continuously improve. Commitment to ethical AI, data privacy, and transparency.
Collaborative mindset with proven success in agile, team-based environments.
Thrives in a fast-paced, evolving environment, proactively seeking opportunities to upskill and enhance processes.
Success Metrics
- Timely delivery of scalable, maintainable AI solutions.
- High system uptime, performance, and cost-efficiency of deployed workloads.
- Consistent adoption of best practices in CI/CD, monitoring, and version control.
- Positive stakeholder feedback and contribution to team documentation, learning, and innovation initiatives.