• Infrastructure Development: Build and maintain Enterprise Feature Stores and real-time data lakes (BigQuery, Pub/Sub) to streamline predictive modeling and NLP workflows. • Design and implement resilient, secure multi-cloud architectures (GCP and Azure) using Terraform (IaC) to support high-availability AI operations. • Operationalize ML models using Python, PySpark, and Kafka for both batch and real-time inference. • Leverage advanced orchestration frameworks such as LangChain, LangGraph, CrewAI, and Vertex AI Agent Builder to develop sophisticated agentic AI solutions. • Implement end-to-end CI/CD pipelines for automated model training, deployment, and seamless rollback capabilities. • Standardize LLMOps and MLOps observability practices using tools like LangSmith and Arize platforms. • Collaborate with data scientists to understand model objectives and translate them into technical specifications. • Deploy AI models into multiple environments using CI/CD pipelines, ensuring seamless integration with existing systems. • Establish strong observability and data governance using tools like Grafana, OpenTelemetry, Splunk or Arize to monitor pipeline health and model behavior. • Implement security measures to protect sensitive data and ensure compliance with industry regulations.

• Own team-level architecture, delivering strategic and tactical software to address defined problems • Begin to influence work outside of your direct team, solving complex problems impacting performance, features, and product design • Support and unblock peers through async reviews, pairing, and mentorship • Work in an async-first way, aligning clearly on scope, tradeoffs, and outcomes • Build across the stack, using TypeScript/React/Node with PostgreSQL, Redis, and Kubernetes under the hood

• Design and carry out electrical technical studies • Monitor and validate design deliverables • Supervise and review studies performed internally or by contract holders • Provide operational support to the Discipline Manager • Apply engineering principles and resolve complex design issues

• Shape the product architecture end-to-end. • Own the data foundation. • Engineer for performance at scale. • Drive technical direction through RFCs. • Ship with product and design, not in a silo. • Use AI as a force multiplier (responsibly). • Hold the quality bar high.