Senior Software Engineer, AI - Augmented Development

We're looking for a senior software engineer who has fundamentally transformed how they work through AI. This isn't about occasionally using Copilot for autocomplete. It's about leveraging AI as a force multiplier across the entire development lifecycle: architecture, implementation, testing, debugging, documentation, and code review. You'll bring this AI-native approach to building high-scale, cloud-based B2B software while also designing and shipping production agentic systems.

• Architect and develop features for a high-scale, cloud-based B2B platform.
• Drive code quality through rigorous testing, thoughtful code reviews, and continuous improvement.
• With a strong product ownership mindset, embed with product, QA, support, and operations to solve complex functional and non-functional challenges.
• Modernise system architecture and integrate with cloud technologies.
• Design and implement agentic architectures: single-agent, multi-agent, hierarchical flows, and tool-rich execution patterns.
• Build production agents for domain-specific workflows, including retrieval, reasoning, tool use, approval chains, and human-in-the-loop interactions.
• Own context engineering end-to-end: prompt design, memory management, retrieval strategies, grounding, and evaluation loops.
• Solve for scale: concurrency, state persistence, streaming, observability, and cost optimisation.
• Translate ambiguous business problems into shipped agents with measurable outcomes.

• 4+ years of software engineering experience with strong fundamentals in at least one major stack (PHP, Ruby on Rails, Node.js/Type Script, Java, or Python).
• 2+ years of hands‑on work in applied AI, LLMs, or agentic system design.
• Demonstrated track record in building and deploying enterprise AI applications, workflow agents, conversational agents, or decision‑support systems.
• Solid experience with cloud platforms (AWS, Azure, or GCP) and MLOps tooling for scalable AI deployment.

• Strong proficiency in Python and agentic orchestration frameworks.
• Fluency with agent patterns (graph/workflow agents, multi‑agent collaboration, tool‑calling protocols) and RAG architectures.
• Deep understanding of prompt and context engineering: when to use structure, when to retrieve, and how to evaluate.
• Strong grasp of data engineering concepts, document indexing, and contextual retrieval.
• Solid foundation in system architecture and design patterns.