AI/ML Engineer

Top 3 requirements to hire: • 3+ years deploying AI/ML applications in production • Python + AWS experience • At least one AWS Associate level certification 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.