Role Overview
We are seeking for our client an experienced AI/ML Engineer with strong expertise in Large Language Models (LLMs), Agentic AI, prompt engineering, model fine-tuning, Reinforcement Learning from Human Feedback (RLHF), Retrieval-Augmented Generation (RAG), and agent orchestration frameworks.
The ideal candidate will have hands-on experience building, fine-tuning, and optimizing AI agents using client's AI ecosystem, including Vertex AI, PaLM, and Gemini. This role requires a deep understanding of transformer architectures, prompt optimization techniques, model evaluation, and emerging agent communication protocols.
The successful candidate will collaborate with cross-functional teams to design, develop, evaluate, and deploy next-generation AI solutions for complex business use cases.
Key Responsibilities

Design, develop, and optimize prompt engineering strategies for large language models.
Build and curate high-quality datasets for Retrieval-Augmented Generation (RAG) and model fine-tuning.
Fine-tune large language models using domain-specific datasets.
Apply RLHF techniques to improve model accuracy and agent decision-making.
Design, develop, and deploy AI agents using Vertex AI, PaLM, and Gemini.
Collaborate with engineering teams to define agent architecture, orchestration strategies, and evaluation frameworks.
Implement Agent-to-Agent (A2A) communication and orchestration mechanisms.
Standardize agent interactions using the Model Context Protocol (MCP).
Develop and maintain scalable machine learning pipelines.
Evaluate model performance and implement continuous optimization strategies.
Conduct experiments involving prompt tuning, chain-of-thought reasoning, and agent workflows.
Stay current with the latest developments in generative AI, agent frameworks, and LLM optimization techniques.

Required Skills and Qualifications
AI/ML Expertise

Minimum 3 years of experience in AI/ML engineering.
Strong understanding of Large Language Models (LLMs).
Experience with prompt engineering and prompt optimization.
Hands-on experience with RAG architectures.
Experience fine-tuning foundation models using domain-specific datasets.
Practical experience applying RLHF techniques.
Knowledge of transformer-based architectures.

Agentic AI Frameworks
Experience with:

Agent Development Kit (ADK)
Agentspace
Agent-to-Agent (A2A) protocols
Model Context Protocol (MCP)

Model Optimization Techniques
Knowledge of:

LoRA (Low-Rank Adaptation)
Quantization
PEFT (Parameter-Efficient Fine-Tuning)
Prompt tuning
Chain-of-thought methodologies

Programming Skills
Strong programming experience in:

Python
TensorFlow
JAX
PyTorch

Language Requirement

Fluent Chinese language skills are mandatory.
Ability to communicate effectively with Chinese-speaking stakeholders and technical teams.

Primary Skills

Prompt Engineering
RAG Dataset Curation
LLM Fine-Tuning
RLHF
Vertex AI
PaLM
Gemini
Agent Architecture
Agent Evaluation
MCP
A2A
ADK
Agentspace
Python
TensorFlow
JAX
PyTorch
LoRA
PEFT
Quantization
Prompt Tuning
Chain-of-Thought Reasoning

Preferred Qualifications

Experience building production-grade AI applications.
Experience developing and deploying AI agents at scale.
Exposure to enterprise AI implementations.
Experience working in cloud-based machine learning environments.

If you are passionate about building next-generation AI solutions and have hands-on experience with LLMs and Agentic AI frameworks, we would love to hear from you.

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