Junior Full-stack AI Application Engineer (RAG & Agentic Systems)

🎯 Role Overview

We are seeking a Senior AI Engineer to lead the architecture, deployment, and optimization of our next-generation generative AI infrastructure. This is a highly technical role where you will design enterprise-grade, localized AI systems. You will move past simple API wrapper applications to build autonomous agentic workflows, custom high-performance RAG pipelines, and highly optimized local LLM deployments running on resource-constrained GPU hardware.

🔑 Key Responsibilities

• Design and implement production-grade Advanced RAG pipelines utilizing Hybrid Search, Re-ranking, Query Expansion, and Multi-vector retrieval .
• Architect structured document ingestion pipelines, mastering advanced chunking strategies and complex metadata filtering schemes.
• Deploy, serve, and optimize state-of-the-art open-source foundational models and Small Language Models (SLMs) such as Qwen, DeepSeek (including V4 architectures), and Codestral .
• Maximize throughput and minimize time-to-first-token (TTFT) on limited GPU infrastructure using advanced serving engines.
• Build context-aware, reliable AI agents capable of autonomous planning, complex tool reasoning, and task execution.
• Leverage advanced multi-agent orchestrations to handle cyclic graph execution and persistent state loops.
• Establish rigorous automated AI quality gates to evaluate hallucination rates, retrieval accuracy, and generation fluency.
• Implement end-to-end trace logging and monitoring systems to flag search degradation and production system failures in real time.

✅ Technical Requirements

• Deep, hands-on experience managing specialized vector databases ( Qdrant, Milvus ) alongside mastery of semantic and recursive text splitting.
• Comprehensive understanding of advanced search mechanisms, including Cross-Encoders vs. Bi-Encoders, Reciprocal Rank Fusion (RRF), and Self-RAG architectures.
• Production-proven proficiency with LangGraph or LlamaIndex Workflows .
• Practical expertise configuring, benchmarking, and maintaining model serving layers through vLLM or llama.cpp (including GGUF/AWQ quantization methods).
• Strong technical English communication skills to document architectural patterns and collaborate with senior engineering stakeholders.

🌟 Nice-to-Have

• Experience constructing and integrating Knowledge Graphs into standard vector search pipelines to preserve complex relational context.
• Basic understanding of CUDA/Triton; the ability to write or customize custom kernels to accelerate inference loops is a significant advantage.
• Deep understanding of handling error-prone AI systems using enterprise patterns like Dead Letter Queues (DLQ), Circuit Breakers, and exponential backoff retry logic .
• Experience with Parameter-Efficient Fine-Tuning ( LoRA / QLoRA ) for small model modules targeting niche downstream tasks.

WHY YOU WILL LOVE WORKING HERE

• Competitive salary and benefits including Premium Healthcare package insurance

• 13 th month salary, Performance bonus

• 16 days annual leave

• Comprehensive Training Courses (Languages, Technical and Soft-skill)

• Language Certificate allowance (TOIEC, IELTS and Japanese)

• Lunch and bus/ parking allowance

• Fun team activities and outings

• Sponsored happy hour and sport clubs

• Comfortable work space and friendly colleagues