Design and develop generative AI infrastructure, including LLM deployment pipelines, vector databases, APIs, and agent orchestration frameworks — used in real-world GenAI platforms such as OpenClaw
Build LLM-powered applications such as chatbots, document processing systems, content generation tools, and intelligent assistants
Design, implement, and maintain retrieval-augmented generation (RAG) pipelines, knowledge bases, and data ingestion workflows
Apply prompt engineering, fine-tuning, and model selection techniques to optimize performance, cost, and latency
Deploy, monitor, and evaluate production-grade GenAI solutions using LLMOps best practices (evaluation, prompt versioning, guardrails)
Ensure security, scalability, reliability, and responsible AI compliance across all AI deployments
Design and implement AI agents and multi-agent systems for workflow automation and decision support
Collaborate with business, product, and engineering teams across subsidiaries to identify and deliver AI use cases
Translate technical AI capabilities into actionable insights for stakeholders and leadership
Track advancements in generative AI and integrate relevant innovations into business solutions
Contribute to AI transformation initiatives and internal knowledge-sharing activities
Requirements
2–5 years of experience in data science, machine learning, or applied AI, with strong Python skills and hands-on experience in large language models, prompt engineering, and retrieval-augmented generation (RAG)
Experience with GenAI frameworks, API development, vector databases, and cloud AI platforms is required
Familiarity with structured and unstructured data, text and document processing, embeddings, AI agents, and standard development tools such as Git/GitHub and Jupyter Notebooks is expected
