• Design and build core platform primitives including provider abstraction layers (OpenAI, Anthropic, Google), structured output validation, streaming infrastructure, and token management systems • Own safety and compliance infrastructure including composable guardrail systems, PII detection/redaction, audit logging, and privacy-first observability that never leaks sensitive data to third parties • Build evaluation infrastructure that enables systematic quality measurement for non-deterministic LLM outputs—datasets, scorers (exact match, LLM-as-judge, schema validation), CI/CD integration, and regression detection • Lead churn containment strategy—design provider adapters and SDK architecture that absorbs rapidly-changing LLM provider SDKs without breaking consuming applications • Architect prompt lifecycle management systems including version control, Langfuse integration, GitHub-based review workflows, and deployment pipelines • Design Agent-as-a-Service infrastructure for long-running async tasks using AWS EventBridge, DynamoDB, and PostgreSQL • Collaborate with consuming teams to understand their needs, onboard them to the platform, and provide technical support • Influence architecture, technology selections, and engineering standards across the broader organization • Create reference implementations and technical documentation that enables other engineers to successfully adopt the platform • Champion quality engineering practices including comprehensive testing, type safety, and observability

• Develop trading systems and monitoring tools for Liquid Air Energy Storage (LAES) plants • Design and implement automated trading systems for UK energy markets • Build real-time bidding, dispatch optimization, and pricing algorithms for LAES assets • Integrate with grid operators and market platforms • Develop monitoring dashboards, alerting systems, and operational analytics for plant performance • Create high-throughput data ingestion and streaming pipelines using Kafka and PostgreSQL / TimescaleDB • Build microservices and APIs for trading platforms, market integration, and system control • Collaborate with DevOps and Data teams for deployment, monitoring, and production support

• Actively participate in development (Java and/or backend), delivering production-ready code. • Perform technical code reviews, ensuring standards, quality, and security. • Define and evolve solution architecture with a focus on simplicity, scalability, and low coupling. • Lead legacy system modernization initiatives, defining strategies such as refactoring, strangler pattern, or encapsulation via APIs. • Support the team in breaking down technical stories, defining tasks, and identifying risks. • Actively participate in production incidents, root cause analysis, and prevention. • Implement and evolve observability practices (logs, metrics, alerts). • Lead the team by technical example: coding, reviewing, and mentoring. • Support developers' technical growth through pair programming and continuous feedback. • Ensure technical alignment without creating bottlenecks or excessive centralization. • Serve as a technical reference for Product Managers, Architecture, and other squads. • Partner with PMs to translate business objectives (OKRs) into technical decisions. • Support technical backlog prioritization by clarifying risks, dependencies, and trade-offs. • Avoid overengineering, prioritizing incremental and sustainable deliveries. • Actively participate in agile rituals, focusing on execution and team flow.

• Develop features and improvements to Dandy’s products in a secure, well-tested, and performant way. • Collaborate with Product Management and other stakeholders within the tech org to maintain a high bar for quality in a fast-paced, iterative environment. • Develop order routing logic to deliver the correct order at the correct time to the right node in the chain. • Partner with manufacturing automation on projects such as CAD -> CAM automation. • Support the ability to provide the business with real-time data on the status of an order. • Build rules and software capabilities smooth demand and overall dynamics of the system to optimize workloads.