• Build and operate scalable backend services and internal APIs for the AI platform. • Integrate LLMs and AI tool execution into reliable, production-ready workflows. • Own production reliability for AI platform infrastructure through observability, alerting, and incident response. • Improve performance and cost efficiency of AI runtimes through profiling, tuning, and capacity planning. • Contribute to shared infra: CI/CD, observability, deployments

• Develop reusable ingestion frameworks (Python/Airflow/AWS Glue) for APIs and unstructured sources beyond Fivetran, handling various data formats (JSON, Parquet, etc.). • Own the end-to-end Medallion (bronze/silver/gold) architecture for core domains, ensuring robust lineage and metadata across diverse data sources. • Implement data observability (native tests, alerts, lineage hooks); lead incident management and root-cause analysis (RCA) for data. • Help standardize reusable “paved-road” patterns (e.g. CI templates, ingestion operators) to improve developer productivity. • Prepare datasets for AI/LLM use cases (feature stores, embeddings/RAG prep).

• Architect and manage AWS infrastructure that supports a high-availability PHP/JavaScript application stack using Terraform to define and provision resources. • Own and improve the CI/CD pipeline built with GitHub Actions and CodePipeline to support reliable deployments with fast feedback to the devs. • Lead initiatives to scale and tune our MySQL-based data layer, including replication, backups, monitoring, and performance optimization. • Ensure our environments meet security and compliance standards (e.g., HIPAA, SOC 2) and participate in security reviews, incident response planning, and disaster recovery readiness. • Design network architecture and IAM policies to minimize risk and follow best practices. • Define and refine lightweight, high-leverage processes —automating where it counts, documenting the important stuff, and avoiding ceremony that doesn't add value. • Expand system observability via metrics, logging, and alerting across our stack. • Advocate for DevOps best practices across engineering.

• Design, build, and maintain the core infrastructure layer supporting GenAI products, including model gateways, prompt/versioning stores, vector databases, and LLM evaluation tools. • Implement secure access controls and authentication mechanisms integrated by default into the AI platform components. • Develop and manage observability, monitoring, and logging solutions for GenAI workloads and infrastructure. • Collaborate closely with product and engineering teams to integrate GenAI infrastructure with agent frameworks, and downstream applications. • Optimize infrastructure for scalability, high availability, cost efficiency for production workloads.