AI Architect

As the fastest-growing, highest-performing charter school network in New York State, Success Academy is reimagining K-12 public education. Nationally recognized for our innovative, whole-child school model and outstanding academic results, Success Academy educators are empowering children from all backgrounds with the knowledge, skills, and self-belief to succeed in college and in life. We now need a results-driven AI Senior ML/AI Architect (AWS) / Integration Specialist to join our AI Enablement team.

Department 

The Technology Division at Success Academy is the chief provider of technology infrastructure, application services, security, and support. The Division partners with teams across the organization to equip students, teachers, leaders, and staff with the innovative technology tools and know-how necessary to achieve academic and operational excellence.

We are seeking a highly accomplished and hands-on Senior ML/AI Architect with deep expertise in Amazon Web Services (AWS). This is a critical technical position within our nascent AI development team. You will be responsible for defining the end-to-end technical strategy, architectural patterns, and implementation blueprints for all AI/Machine Learning initiatives, ensuring their scalability, security, cost-efficiency, and operational excellence within the AWS cloud environment.

Requirements:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field. Ph.D. is a significant plus.
• 4+ years of hands-on experience in software engineering and cloud architecture, with at least 7 years directly architecting and deploying production-grade, large-scale AI/ML solutions on AWS.
• Expert-level proficiency in Python and extensive experience with core ML/DL frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Deep and demonstrable expertise across a broad range of AWS AI/ML services, including but not limited to:

• Amazon SageMaker (Studio, Notebook Instances, Training Jobs, Hyperparameter Tuning, Endpoints, Pipelines, Model Registry, Feature Store, Model Monitor)
• AWS AI Services (e.g., Rekognition, Comprehend, Textract, Polly, Transcribe, Lex, Personalize, Forecast, Translate, Kendra)
• Amazon Bedrock (especially for Generative AI use cases, Foundation Model integration, Guardrails for Bedrock)

Extensive experience with AWS core infrastructure services:

• Compute: EC2 (various instance types, incl. GPU instances), ECS, EKS, Lambda, Fargate.
• Storage: S3 (including S3 Glacier for archiving), EBS, EFS.
• Networking: VPC, Subnets, Security Groups, NACLs, Route 53, Load Balancers (ALB/NLB), API Gateway.
• Database: RDS (PostgreSQL/MySQL), DynamoDB, Redshift, Aurora.
• Identity & Access Management (IAM): Policies, Roles, Users, Groups, Service Control Policies (SCPs).

Proven experience designing and implementing MLOps pipelines on AWS:

• CI/CD tooling: AWS CodePipeline, CodeBuild, CodeDeploy, Git (GitHub/GitLab/AWS CodeCommit).
• Workflow Orchestration: AWS Step Functions, Apache Airflow (running on EC2/EKS).
• Containerization & Orchestration: Docker, Kubernetes (Amazon EKS strongly preferred).
• Infrastructure as Code (IaC): AWS CloudFormation or Terraform.

Robust understanding of data engineering principles and AWS data services: AWS Glue, Amazon Athena, Amazon EMR, AWS Lake Formation.

Demonstrable expertise in AWS security best practices, including data encryption (KMS), network security, IAM governance, and security auditing (CloudTrail, CloudWatch Logs, GuardDuty).

Experience with cost optimization strategies for AWS AI/ML workloads.

AWS Certifications (e.g., AWS Certified Machine Learning – Specialty, AWS Certified Solutions Architect – Professional) are highly desirable.

Responsibilities:

AWS AI/ML Architectural Leadership:

• Design, architect, and implement highly scalable, resilient, and secure end-to-end ML architectures leveraging the full suite of AWS AI/ML services (e.g., Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, Amazon Textract, Amazon Polly, Amazon Transcribe, Amazon Lex, Amazon Personalize, Amazon Forecast).
• Define the reference architectures and best practices for building, training, deploying, and managing machine learning models on AWS, including batch inference, real-time inference endpoints (SageMaker Endpoints), and edge deployment strategies (AWS IoT Greengrass).
• Lead the selection and integration of appropriate AWS services for specific AI/ML use cases, considering factors like performance, cost, security, and maintainability.

MLOps and DevOps for AWS:

• Architect and implement robust MLOps (Machine Learning Operations) pipelines on AWS, utilizing services such as AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy, AWS Step Functions, and AWS Lambda for automated CI/CD of ML models.
• Design and implement model monitoring and retraining strategies using Amazon CloudWatch, Amazon SageMaker Model Monitor, and AWS Lambda, including anomaly detection for data drift and model performance degradation.
• Champion Infrastructure as Code (IaC) principles using AWS CloudFormation or Terraform for reproducible and automated provisioning of AWS resources for ML workloads.

Data Strategy & Engineering on AWS:

• Collaborate extensively with Data Engineers to design secure, scalable, and performant data architectures on AWS, including Amazon S3 (for data lakes), AWS Glue (ETL), Amazon Athena, Amazon Redshift, Amazon EMR, and potentially Amazon DynamoDB or Amazon Redshift for feature serving.
• Define data governance strategies, data versioning (e.g., using DVC on S3), and data quality frameworks for ML datasets on AWS.

Security, Compliance & Cost Optimization:

• Design and enforce stringent security controls for AI/ML solutions on AWS, adhering to the AWS Well-Architected Framework (Security Pillar), IAM best practices (least privilege, MFA), VPC segmentation, Security Groups, NACLs, KMS for encryption (at rest and in transit), and AWS Config/CloudTrail for auditing.
• Ensure compliance with educational data privacy regulations such as FERPA through robust data anonymization, access controls, and logging practices on AWS.
• Optimize AWS resource utilization and costs for ML training, inference, and storage through intelligent architectural choices, instance type selection (e.g., EC2 instances with GPUs, SageMaker instances), and auto-scaling configurations.

Technical Guidance & Mentorship:

• Provide expert-level technical guidance, mentorship, and architectural reviews to data scientists and ML engineers on AWS-specific design patterns, best practices, and troubleshooting.
• Drive technical innovation by staying abreast of the latest AWS AI/ML services, industry trends, and research.

Preferred Qualifications:

• Prior experience in the EdTech domain, K-12 education, or working with highly sensitive data (e.g., student PII).
• Experience with real-time streaming data for ML (e.g., Amazon Kinesis, Apache Kafka).
• Familiarity with distributed training frameworks and techniques (e.g., Horovod).
• Active participation in the AWS Machine Learning community (e.g., re:Invent presentations, blog posts).
• Experience with serverless architectures for ML inference (e.g., Lambda functions with SageMaker endpoints).

Success Academy Charter Schools is an equal opportunity employer and actively encourages applications from people of all backgrounds. Compensation is competitive and commensurate with experience. Success Academy offers a full benefits program and opportunities for professional growth.