AI/ML Engineer

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.

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.

Working Conditions:

• Hybrid work model (2 3 days on campus).
• Collaborative, agile team culture with regular code reviews and paired development.