AI Engineer

• 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 Application Developer – AI Finance Agent

• Analyze business documents such as insurance policies, claim forms, and financial reports to understand structure, logic, and extract key information.
• Design structured test datasets, define ground truth data for validation, and create Q&A pairs to evaluate AI models’ reasoning and comprehension.
• Run systematic tests on various AI models, evaluate performance, and identify potential gaps and areas for improvement.
• Challenge AI systems with edge cases and complex scenarios to expose weaknesses and help refine model behavior.
• Develop scripts to automate evaluation processes (optional for those with coding skills)
• Assist in building evaluation pipelines (optional for those with coding skills)
• Support data processing and basic model testing infrastructure (optional for those with coding skills)

AI Engineer Intern

• 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 agents, workflows, tools and automations that solve real customer, product and operations problems.
• Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows.
• Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence.
• Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production.
• Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust.
• Turn manual processes into reliable AI-enabled systems that can scale.

Applied AI Engineer

• 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

Mobile Developer, AI Neobank App

LLM Engineer

• Develop and advance speech, audio, and multimodal LLMs that power next-generation voice-first agentic AI experiences
• Leverage AI-native and agentic workflows to accelerate research, development, evaluation, and deployment of AI systems
• Design and deploy AI models and platforms with strong consideration for AI safety, security, privacy, and Sovereign AI requirements
• Establish and drive benchmarking frameworks for voice agents, including speech quality, reasoning, tool use, latency, reliability, and user experience
• Lead technical initiatives, mentor engineers, and foster a One Team culture through close collaboration across research, engineering, product, and customer teams

Speech LLM Engineer, Voice-First AI

• 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.

Applied AI Engineer – Finance Super App

• Design, build, test and operate AI services and workflows from proof of concept through production deployment.
• Develop capabilities across classification, extraction, summarisation, entity resolution, stakeholder mapping, retrieval, risk detection and insight generation.
• Select the appropriate combination of deterministic software, machine learning, retrieval, LLMs, agents and human review for each use case.
• Build clear APIs and integrations that connect AI capabilities to Genie and related internal systems.
• Own the quality, reliability and maintainability of the components you ship, including production support and continuous improvement.
• Build and improve pipelines for ingestion, cleaning, enrichment, labelling, chunking, indexing and retrieval.
• Protect sensitive and customer-specific data through secure handling, access controls, workspace boundaries and careful use of external model providers.
• Identify and mitigate risks including prompt injection, data leakage, stale context, unsupported claims, excessive autonomy and unsafe tool permissions.
• Translate ambiguous customer and business workflows into clear technical requirements, experiments and acceptance criteria.
• Communicate technical trade-offs, limitations and evidence clearly to both technical and non-technical colleagues.
• Maintain clear technical documentation, implementation notes and decision records in shared systems such as Jira and Confluence.
• Participate actively in code reviews, design reviews, sprint planning, incident learning and architectural discussions.

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