Jr. AI Developer - Korean Bilingual MUST

SBT Global•Plano, TX

6h•Onsite

About The Position

We are seeking a motivated Junior Generative AI & Agentic AI Developer to join the team in Plano, Texas. The role focuses on designing, developing, and supporting AI-powered enterprise applications using Generative AI, large language models (LLMs), and Agentic AI technologies. The developer will collaborate with application developers, business stakeholders, and AI engineers to deliver intelligent automation solutions that improve enterprise productivity and business processes. This position is suited to a fast learner interested in developing expertise in Generative AI applications, AI agents, and enterprise AI solutions. The position is offered for a one-year contract, renewable, with a pay rate of up to $7,699.43 per month, depending on experience. Korean bilingual ability is identified as preferred in the role overview and as a preferred qualification later in the posting.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent professional experience.
Working knowledge of Python.
Understanding of Generative AI concepts, LLMs, and AI application development.
Experience or academic/project exposure to LLM-based application development.
Understanding of RAG architecture and AI knowledge retrieval concepts.
Familiarity with prompt engineering techniques and LLM response optimization.
Basic understanding of AI agent concepts, including workflow automation, reasoning, and tool integration.
Experience with REST API development and integration.
Understanding of database concepts, including SQL, data modeling, and data processing.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
Exposure to AI/ML frameworks and libraries such as LangChain, PyTorch, TensorFlow, or similar technologies.
Basic knowledge of software development practices, including Git, version control, and code reviews.
Understanding of DevOps concepts, including CI/CD pipelines and containerization; Docker is preferred.
Experience working with Linux environments.
Strong problem-solving skills and ability to learn emerging technologies quickly.
Excellent verbal and written communication skills.

Nice To Haves

Experience developing enterprise Generative AI applications or AI automation solutions.
Hands-on experience with Agentic AI frameworks such as LangGraph, AutoGen, CrewAI, or similar technologies.
Experience with cloud AI services such as Amazon Bedrock, Azure OpenAI Service, AWS SageMaker, or Azure AI Studio.
Experience with vector databases such as FAISS, ChromaDB, Pinecone, or OpenSearch Vector Engine.
Experience building AI-powered chatbots, copilots, or intelligent assistants.
Knowledge of Java/Spring Boot enterprise application development.
Experience with enterprise system integration and application operations.
English-Korean bilingual communication skills.

Responsibilities

Design, develop, and maintain Generative AI applications using Python and modern AI frameworks.
Develop enterprise solutions using LLM platforms such as OpenAI, Azure OpenAI, and Amazon Bedrock.
Build Retrieval-Augmented Generation (RAG) solutions using enterprise data sources, vector databases, embeddings, and knowledge retrieval techniques.
Design and develop Agentic AI workflows supporting autonomous task execution, reasoning, planning, and tool use.
Develop AI agents using frameworks such as LangChain, LangGraph, AutoGen, and CrewAI.
Perform prompt engineering and optimize LLM responses for accuracy, reliability, and business use cases.
Integrate Generative AI capabilities into existing enterprise applications through REST APIs and backend services.
Develop AI-powered chatbots, copilots, and intelligent automation solutions.
Support AI model integration with business systems, databases, and enterprise platforms.
Design and implement API services using Python frameworks such as FastAPI or Flask.
Develop data processing pipelines for AI applications, including data preparation, transformation, and indexing.
Work with relational and vector databases to support AI knowledge management solutions.
Support AI model evaluation, testing, monitoring, and continuous improvement.
Assist with deployment and operation of AI applications in cloud environments.
Participate in MLOps/LLMOps activities, including CI/CD automation, model lifecycle management, and application monitoring.
Collaborate with application developers, DevOps engineers, and business teams to deliver AI-driven solutions.
Troubleshoot application issues and improve system performance and reliability.
Document AI solutions, architecture, development processes, and operational procedures.
Participate in Agile/Scrum ceremonies, sprint planning, and technical discussions.
Develop and deploy enterprise Generative AI applications to improve business productivity; build reliable agent workflows and effective RAG solutions; improve LLM application accuracy; create scalable architectures and reusable components; collaborate with business users and technical teams; and continually learn and apply emerging AI technologies.