Performance and scale. Identify bottlenecks, improve database access patterns, and optimise latency and throughput as transaction volumes grow.
Production reliability. Build useful monitoring and alerting, investigate incidents, and turn root cause analysis into durable improvements.
Engineering quality. Write maintainable code, build meaningful unit, integration, and performance tests, and strengthen delivery through code reviews and CI/CD.
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Accelerate your path toward senior solutions engineering or product roles by owning measurable demo success metrics. Document technical wins and case studies that highlight your impact on deals and deployments.
Gain visibility with product and engineering teams by feeding back field insights that influence roadmap priorities. Learn how product decisions are made and how to translate customer requests into deliverable scope.
Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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