Senior AI Engineer

We are seeking a highly skilled Senior AI Engineer with at least 3 years of experience to lead the integration of Generative AI into our enterprise ecosystem. You will design, build, and optimize AI-powered platforms that enable our teams and customers to interact with internal documentation and data using natural language.

• AI Architecture & Implementation: Design and implement system architectures for AI knowledge retrieval using Retrieval-Augmented Generation (RAG) pipelines.
• LLM Integration: Integrate multiple LLM providers including OpenAI (GPT), Anthropic (Claude), and Google Gemini.
• Vector Database Management: Build and maintain semantic search capabilities using vector databases such as Pinecone, FAISS, or Weaviate.
• Agentic Workflows: Develop AI Agent workflows for automated tasks including document summarization and intelligent knowledge query automation.
• Prompt Engineering: Apply advanced prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought) to optimize model responses and mitigate hallucinations.
• Platform Integration: Build scalable backend APIs using Node.js, Python, or Spring Boot to integrate AI services with existing web applications and internal tools.
• Custom Model Development: Possibility of creating new AI models aligned with clients' specific business requirements.
• Own the full lifecycle of LLM initiatives: opportunity sizing, experiment design, dataset curation and labeling, offline evaluations, A/B tests, launch, and post-launch monitoring.
• Build LLM systems such as RAG, function/tool use, and lightweight fine-tuning (LoRA/PEFT) for domain-specific tasks.
• Design rigorous evaluation frameworks for generative systems — covering truthfulness, task success, hallucination rate, safety guardrails, latency, and cost per output token — and maintain dashboards and alerts.
• Productionize models and pipelines with robust data preprocessing, vector indexing, caching, fallback policies, and prompt/version management.
• Optimize for quality and cost via model selection and routing, context window strategy, compression, and inference tuning.
• Mentor DS/ML peers; contribute to best practices, documentation, and design reviews.
• Shift Requirements: The role is fully remote / in office, requiring a minimum of 8 hours of work per day, of which 4 hours must overlap with 8:00 PM to 12:00 AM Vietnam time zone

• Proficiency in Python (FastAPI / Flask) or Node.js (NestJS).
• Direct experience building RAG pipelines and managing vector embeddings.
• Strong understanding of REST APIs, microservices architecture, and CI/CD tools.
• Experience with cloud infrastructure management, specifically AWS (EC2, S3, RDS, Lambda).
• Expert-level Python and ML stack: PyTorch, Hugging Face Transformers; solid SQL; comfort with Spark or a similar big-data tool.
• Practical experience with RAG — vector DBs such as FAISS, Milvus, or pgvector — including chunking and embedding strategy, prompt and tool design, and evaluation at scale.
• Experience with multimodal LLMs (vision / audio) and tool-use / agentic patterns.
  Preferred Skills
  • Experience with AI frameworks such as LangChain, LangChain4j, or LlamaIndex.
  • Familiarity with Java / Spring Boot for enterprise AI integration.
  • Knowledge of caching systems like Redis for maintaining conversation memory and improving response times.