Mobile Engineer - AI Finance Agent
Build and ship mobile features for Android and iOS to create an AI Finance Agent. Collaborate with product and design teams to turn complex financial journeys into intuitive mobile experiences.
Remote AI Engineer Jobs in China
133 AI Engineer jobs in China available for remote work from home. Apply for positions such as Mobile Engineer - AI Finance Agent, Mobile Engineer - AI Finance Agent, Android Software Engineer - AI Finance Agent and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Build and ship mobile features for Android and iOS to create an AI Finance Agent. Collaborate with product and design teams to turn complex financial journeys into intuitive mobile experiences.
Develop and ship Android features for an AI Neobank app using Kotlin and Jetpack Compose. Collaborate with product and design teams to create intuitive financial service flows and optimize app performance and reliability.
Develop and ship native Android features for an AI Finance Agent using Kotlin and Jetpack Compose. Collaborate with product and design teams to optimize user journeys and integrate backend APIs for financial services.
Design and build scalable data ingestion, cleaning, and LLM enhancement pipelines to power AI applications. Implement AI guardrails and RAG architectures to ensure the accuracy and reliability of AI-ready datasets.
Build AI-native systems and agents to automate financial workflows for a Neobank app. Design and implement reliable, scalable AI tools for onboarding, KYC, risk review, and customer support.
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Build and maintain backend services, APIs, and workflows for an AI Neobank app covering insurance, payments, and financial services. Design reliable systems for transactions, partner integrations, and reconciliation while improving system observability and security.
Build AI-native systems, agents, and automated workflows to optimize financial services and internal operations. Design and productionize scalable AI integrations with LLMs and internal data while ensuring reliability and safety.
Build end-to-end customer-facing and internal product features for an AI Neobank app. Develop web flows, backend APIs, and operational tools while collaborating with cross-functional teams to launch financial services quickly.
Build and ship native Android features for an AI Neobank app using Kotlin and Jetpack Compose. Collaborate with product and design teams to create stable, secure, and high-performance financial service user flows.
Set the technical direction and architecture for complex Generative AI and agentic systems while remaining hands-on in development. Partner with client leadership to translate business strategies into scalable, production-ready AI solutions.
Design, build, and deploy production-ready AI applications including RAG-powered solutions and conversational AI on major cloud platforms. Collaborate with cross-functional teams to automate insurance processes such as claims and underwriting.
Support the development of AI and Machine Learning solutions, including building prototypes using Generative AI and LLMs. Responsibilities include data analysis, creating AI workflows, and collaborating with engineering and business teams.
Design, implement, and deploy end-to-end AI/ML systems and agentic workflows into production environments. Build supporting data pipelines, retrieval layers, and observability frameworks to ensure system reliability and cost-effectiveness.
Design, develop, and deploy scalable cloud-native applications and intelligent AI solutions. Integrate Generative AI capabilities and build end-to-end applications using AWS services and microservices architecture.
Lead the end-to-end delivery and architecture of enterprise AI/ML solutions, specifically focusing on LLM applications and RAG. Provide technical guidance and mentoring to engineering teams while remaining hands-on with prototyping and troubleshooting.
Design and implement production-ready AI agent workflows using KPU technology to solve enterprise problems. Build evaluation frameworks to measure AI performance and optimize workflows for reliability and explainability.
Design and build LLM-powered autonomous hacking agents capable of performing full-scale offensive security attacks with minimal human intervention. Develop agent architectures, integrate offensive security toolchains, and build evaluation harnesses to measure AI performance.
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Architect and deploy production-grade RAG pipelines and agentic workflows to process complex multimodal data for government and military clients. Balance high-level R&D and experimentation with the delivery of scalable, production-ready software on AWS.
You will own the end-to-end development of AI features, from model behavior and system orchestration to production reliability. This involves translating ambiguous product problems into robust, scalable, and high-performance AI systems.
The role involves leading the technical direction of AI agents responsible for translating natural language into production-ready applications, focusing on defining patterns for LLM interaction, context maintenance, and multi-step workflow orchestration. This includes owning the technical vision for how agents reason about and generate full-stack applications, driving initiatives that influence the broader AI strategy.
As a Senior Applied AI Engineer, you will develop AI agents that transform natural language into production-ready applications and tackle challenges related to maintaining context across large codebases. You will also collaborate with teams to ensure AI-driven features are efficient and well-monitored in deployment.
Build and deploy full-stack applications that incorporate machine learning models. Collaborate with cross-functional teams to deliver polished, end-to-end solutions.
Design, build, and ship production-grade machine learning models for high-impact use cases while leading the end-to-end ML lifecycle. Collaborate with cross-functional teams to drive AI initiatives and mentor junior engineers.
Build and deploy full-stack applications that incorporate machine learning models. Collaborate with cross-functional teams to deliver polished, end-to-end solutions.
You will design, build, and ship production-grade ML models for high-impact use cases while owning the end-to-end ML lifecycle. Collaborate across product, engineering, and data teams to drive AI initiatives and mentor junior engineers.
The role involves fine-tuning and improving self-hosted large language models to enhance conversational quality and personality consistency. You will manage the full lifecycle of model development, from training and debugging on GPUs to production deployment and performance evaluation.
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You will own the architecture and implementation of Elixir-based applications while managing autonomous coding-agent workflows to deliver reliable software. The role involves translating market needs into technical specifications and maintaining proactive communication with clients and the team.
Own and improve the client’s Quickbase systems while building a broader internal operations platform, including workflows, automations, integrations, dashboards, and web-based tools. Assess business processes and determine when to improve Quickbase, build custom software, integrate other systems, or apply AI, while protecting sensitive data and documenting key decisions.
Design, build, and deploy scalable features powered by large language models, including prompt pipelines, evaluation frameworks, fine-tuned models, and retrieval systems. Integrate AI capabilities into production systems and APIs, optimizing performance, latency, and cost while collaborating with product and engineering teams.
Lead the technical bring-up and integration of large-scale GPU compute clusters from physical installation to production readiness. Provide Tier 3 technical support, performance optimization, and root cause analysis for complex production incidents.
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