Troubleshoot, debug, optimise, and enhance application performance, reliability, and scalability.
Manage assigned development tasks and deliver project milestones within agreed timelines.
Collaborate closely with Software Engineers, Business Analysts, Technical Specialists, and project stakeholders throughout the Software Development Life Cycle (SDLC).
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Partner with AI engineers and developers — including agentic AI initiatives such as multi-agent orchestration systems, natural-language query layers over real-time vision event data, and LLM tool-use and automation — to scope, estimate, and sequence work accurately.
Develop detailed project plans, timelines, and resource allocation; monitor progress and report against KPIs.
Serve as the primary point of contact for clients, managing communication and expectations across technical and business audiences.
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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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