AI Jobs in South Korea

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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The Senior AI Agent Engineer on the Intelligence Service team is responsible for designing and refining the RAG-based agent flow of an interactive knowledge agent, covering the process from query understanding to planning, tool routing, retrieval, and response generation. They optimize multi-turn conversation understanding and retrieval linkage, implement response quality control logics including grounding, answer verification, guardrails, and fallback mechanisms to defend against hallucination, and establish evaluation harnesses, regression testing, and A/B testing systems for answer quality in terms of faithfulness and relevancy. They also build backend infrastructure necessary for production operations such as API contracts, caching, configuration/prompt registry, and admin APIs. Furthermore, they analyze and improve response quality, latency, and failure cases through operational logs and quality metrics. The role includes leading design reviews and technical decision-making within the team, connecting complex problems to reusable system improvements as a senior technical pillar of the team.

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Design, develop, and operate backend systems for domain-specific collection search services including news, places, securities, sports, music, and movies. Design and standardize search architectures based on OpenSearch and MongoDB Atlas, including indexing and retrieval structures, to enable rapid expansion of new collections. Analyze search quality and maintain metrics such as nDCG, recall, MRR, and CTR to improve search accuracy, latency, and handle failure cases. Develop and fine-tune ranking models, reranking, embeddings, semantic search, and recommendation logic, focusing on top accuracy for priority collections. Build robust backend infrastructure required for stable production operation, including API contracts, caches, configuration registries, and administrative APIs. Lead technical decision-making processes, conduct design reviews within the team, and address complex problems by improving reusable systems.

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Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.

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The Forward Deployed Engineer Lead is responsible for partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. They architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. The role involves collaborating with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation. They own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, they shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer on Reflection's Applied AI team, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments, including public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. You will also contribute to evolving playbooks, processes, and best practices as part of a growing Forward Deployed Engineering organization.

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As a Forward Deployed Engineer Lead, you will own the end-to-end technical strategy, execution, and delivery of complex agentic applications, from early pre-sales discovery through production deployment. Responsibilities include partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. You will architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. Collaboration with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation is required. You will own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, you will shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer at Reflection, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments such as public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. Additionally, you will contribute to evolving playbooks, processes, and best practices as part of the growing Forward Deployed Engineering organization.

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Research and develop latest TTS models based on LLM and Flow Matching; develop and advance emotion controllable TTS models; build and improve quality of speech synthesis data using latest generative models; develop and apply multilingual and multi-speaker TTS models to services; optimize TTS models for server and on-device environments; develop real-time (streaming) speech synthesis systems and optimize latency; improve inference and training pipelines to enhance speech generation quality.