JOB RESPONSIBILITIES
- Lead the design, development, and maintenance of AI-powered products using Large Language Models (LLMs).
- Architect and own end-to-end Retrieval-Augmented Generation (RAG) systems for complex business applications.
- Design and build sophisticated AI Agents and multi-step AI workflows for real-world production use cases.
- Lead fine-tuning of domain-specific models using LoRA, QLoRA, PEFT, or similar approaches; define fine-tuning strategies.
- Architect, curate, and govern datasets for model training and evaluation; establish data quality standards.
- Design and own evaluation and benchmarking pipelines to measure AI quality, accuracy, and performance at scale.
- Research, evaluate, and productionize new AI techniques, open-source models, and agent-based systems.
- Provide technical leadership and mentorship to junior and middle-level engineers.
- Collaborate closely with product, engineering, and business teams to define AI solutions and drive delivery.
- Define and enforce testing, documentation, and AI engineering best practices across the team.
- Contribute to architectural decisions, technical roadmaps, and AI strategy.
Mandatory Requirements
- Degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
- 3 – 6+ years of experience in AI, Machine Learning, or Software Engineering, with at least 2 years at a senior level.
- Expert-level Python programming skills; solid understanding of software design principles.
- Deep hands-on experience with LLMs, embeddings, vector databases, and production RAG systems.
- Proven track record of building and shipping AI applications or AI-powered products in real-world, production environments.
- Strong familiarity with open-source LLMs such as Llama, Qwen, DeepSeek, Gemma, Mistral, or similar models.
- Solid experience with LoRA/QLoRA fine-tuning, PEFT, AI evaluation and benchmarking, and dataset preparation for AI training.
- Deep understanding of prompt engineering, AI application design, and AI system architecture.
- Experience mentoring engineers and leading technical projects end-to-end.
- Strong English communication skills — both reading and writing; able to communicate technical concepts clearly.
- Strong problem-solving, ownership mindset, and ability to drive decisions under ambiguity.
Nice To Have
- Experience with AI Agents, LangGraph, LangChain, CrewAI, MCP, or similar agentic frameworks.
- Experience leading fine-tuning projects for domain-specific open-source models at scale.
- Experience designing and maintaining evaluation and benchmark pipelines in production.
- Experience with synthetic data generation, data flywheel design, and dataset curation strategies.
- Experience working with OCR, document processing, or knowledge extraction systems.
- Experience contributing to open-source AI projects or communities.
- Experience building and growing AI products with a significant active user base.
- Experience with MLOps, model serving infrastructure, or AI system observability.
What You Will Work On
- Owning and scaling production-grade AI products and architectures.
- Defining and leading AI workflows and AI Agent system design.
- Driving fine-tuning and evaluation strategies for domain-specific AI models.
- Building enterprise-grade RAG systems that power real business outcomes.
- Dataset engineering, AI quality evaluation, and continuous model improvement loops.
- End-to-end AI product development lifecycle — from research to production.
HOW TO APPLY
Step 1: Submit Your CV
Apply directly through the Innotech Vietnam Corporation website by uploading your updated CV to the careers section.
Step 2: Phone Screening
If shortlisted, you will receive a call from the HR team for a brief survey and initial screening.
Step 3: Interview
Qualified candidates will be invited to an interview to further assess suitability for the role.
Step 4: Onboarding
Successful applicants will receive an offer and begin the onboarding process to officially join the team.
