AI​/ML Engineer

This range is provided by Lead Stack Inc. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$60.00/hr - $65.00/hr

Duration: 12 months

Location: Stanford, CA 94305 (Hybrid)

The AI/ML Engineer will be a key technical contributor driving CGOE’s AI transformation initiatives, with a focus on building and deploying intelligent, cloud-native applications including GenAI-powered systems, retrieval-augmented assistants, and data-driven automation workflows. Working at the intersection of machine learning, cloud engineering, and educational innovation, this role converts complex requirements into scalable, secure, and maintainable AWS-native AI systems that enhance teaching, learning, and operations across CGOE’s global online programs.

• 3+ years deploying AI/ML applications in production environments.
• Strong experience with Python and AWS (serverless, microservices, CI/CD, IAM).
• At least one AWS Associate-level certification (e.g., Solutions Architect Associate, Developer Associate, Sys Ops Administrator Associate, Data Engineer Associate).

• 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., Model Context Protocol/MCP) using modern frameworks such as Lang Chain and Llama Index or similar.
• 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.
• Architect and manage scalable AI workloads on AWS leveraging services such as Sage Maker, Bedrock, API Gateway, Event Bridge, and IAM-based security.
• Build microservices and APIs to integrate AI models into applications and backend systems.
• Develop automated CI/CD pipelines to ensure continuous delivery, observability, and monitoring of deployed workloads (e.g., Git Hub Actions, Code Pipeline).
• Apply containerization best practices using Docker and manage workloads via AWS Fargate and ECS for scalable, serverless orchestration and reproducibility.
• Ensure compliance with (e.g., FERPA, GDPR-style requirements) for secure data handling and governance.
• Collaborate with cross-functional teams (engineering, product, academic stakeholders, operations) to deliver integrated and impactful AI solutions.
• Use Git-based version control and follow 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, contributing to documentation, internal knowledge sharing, and mentoring as the team scales.

• Bachelor’s degree in Computer Science, AI/ML, Data Engineering, or a related field (Master’s preferred).
• At least one AWS Associate-level certification required; professional-level or specialty certifications (e.g., Machine Learning Specialty, Advanced Networking, Security) are a plus.

• 3+ years of experience developing and deploying AI/ML-driven applications in production environments.
• 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.

• Languages:
  Python (required); familiarity with Go, Rust, R, or Type Script preferred.
• AI/ML Frameworks:
  PyTorch, Tensor Flow, Lang Chain, Llama Index, or similar libraries for RAG and agentic workflows.
• Cloud &…