Careers at the Planet group

AI/ML Engineer

Stanford, CA 94305

Posted: 01/08/2026 Employment Type: Contract Job Category: Technology Job Number: 642611 Is job remote?: No

Job Description


Job Title: AI/ML Engineer
Location: Stanford, CA
Duration: 12 months
Pay Rate: $55 to $60/hour (DOE) 

Position Overview:
The AI/ML Engineer is a key technical contributor driving CGOE’s 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 CGOE’s 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 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.

Preferred Qualifications:
  • 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.
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