AI Engineer

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration.
• Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP.
• Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift.
• Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts.
• Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output.
• Review AI-generated code before it ships for correctness, security, and type safety.
• Operate what you ship: monitoring, reliability, cost, and failure modes in production.
• Contribute to shared engineering standards, playbooks, and internal tooling.

Applied AI Engineer

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Developer – AI Neobank App

Applied AI Engineer – Finance Super App

• Build AI-powered product features integrated into real insurance workflows.
• Design and optimize LLM-based interactions across customer and internal systems.
• Develop prompts, reasoning flows, and structured AI outputs for production use cases.
• Evaluate model performance using real-world data, feedback loops, and experiments.
• Improve reliability of AI outputs through guardrails, fallback logic, and validation layers.
• Work closely with backend engineers to integrate AI capabilities into APIs and workflows.
• Design and implement AI-driven automation features (chat, recommendations, decision support).
• Analyze user behavior and model responses to continuously improve AI performance.
• Collaborate with product, design, and engineering teams to ship end-to-end AI features.
• Contribute to AI system design decisions focused on usability and real-world impact.

• Work directly with teams and customers to identify high-impact opportunities for AI and workflow automation.
• Design, build, and deploy AI agents and intelligent systems into production environments.
• Lead discovery sessions to understand workflows, pain points, and business requirements.
• Translate complex business challenges into practical technical solutions.
• Rapidly prototype ideas and iterate based on real-world feedback.
• Integrate AI solutions with existing systems, applications, and data sources.
• Build reusable frameworks and components that accelerate future AI initiatives.
• Monitor adoption, performance, and effectiveness of deployed solutions.
• Collaborate across technical and non-technical teams to deliver meaningful outcomes.
• Stay current with emerging AI technologies and identify opportunities to apply them.

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