Job Purpose
As a core technical contributor, you will be instrumental in the design, development, operation, and optimization of our Agentic AI systems, from Proof-of-Concept (PoC) to full-scale Production, with the primary goals:
- Establishing the AI Agent pipeline: From data ingestion and model training to CI/CD and monitoring, ensuring operational efficiency (response time, accuracy, availability).
- Continuous algorithm and architecture optimization: Minimizing manual intervention while enhancing solution stability and scalability.
Providing technical support and knowledge transfer: Enabling the operations team to effectively apply these solutions in real business processes.
Key Responsibilities
Agent Architecture Design
- Evaluate use cases, propose PoCs, and define modular components (decomposition, memory, planning, execution, feedback loop).
- Select appropriate frameworks and tools (e.g., LangChain, ReAct, multi-agent orchestration).
Solution Lifecycle Management
- Build and operate automated MLOps/ LLMOps pipelines: data ingestion feature store training containerization deployment monitoring re-training.
- Guide DevOps/Full-stack teams on integrating and operating models in a production environment to ensure stable agent performance.
Optimization & Scaling
- Monitor agent performance in live environments, analyzing logs and metrics to continuously optimize algorithms and systems.
- Collaborate with the Platform/Infra team to design and maintain shared component libraries (templates, connectors, wrappers) to accelerate the development, deployment, and reusability of new agents.
Team Capability Development
- Organize and conduct internal workshops and training sessions on agent architecture, MLOps, and multi-agent coordination.
Job Requirements
- Bachelor's degree or higher in Computer Science, Information Technology, Mathematics, or a related field.
- Strong foundational and practical experience with Machine Learning and Deep Learning.
- Proven experience deploying and operating models on at least one cloud platform (AWS/GCP/Azure ML).
- Prior experience with LLMOps/ MLOps and participation in Agentic AI PoCs is a plus.
- Proficiency in Python and ML Fundamentals.
- Solid understanding of basic ML concepts: supervised/unsupervised learning, regression, classification, clustering.
- Knowledge of LLM principles (Transformer, attention).
- Skills in optimizing prompts and evaluating output quality.
- Strong analytical and problem-solving skills.
- Ability to work independently.
- Experience working in an Agile/Scrum environment.
- Quick to learn and adapt.
- Willingness to research and experiment with new solutions.
- LLM fine-tuning experience (Nice to have).
Bng vic gi h s ng tuyn n PNJ, ti xc nhn c, hiu r v ng vi Thng bo Chnh sch thu thp v x l d liu c nhn ca PNJ.
Link Thng bo: bit.ly/nghidinh13
