AI Engineer

Experience:

• 4+ years in AI/ML engineering with a real track record of production systems, not prototypes. You can point to things running in production with users and business impact behind them.
• Banking, financial services, or another regulated, security-sensitive industry is a strong plus.
• Bonus: experience as a forward-deployed / solutions / embedded engineer where you owned a customer or business outcome directly.

Core Skills:

• Production-grade LangChain/LangGraph — multi-agent architectures, function calling, tool integration, stateful graphs. This is the bar.
• Strong command of advanced RAG pipeline design and agent orchestration.
• Computer Vision / VLM for OCR, document intelligence, and image analysis.
• LLMOps/MLOps: model serving (FastAPI, vLLM, Triton), containerization (Docker, Kubernetes), CI/CD, evaluation and guardrails.
• Databricks (Delta Lake, MLflow) for data processing and scaled deployment.

How You Operate:

• Outcome-obsessed. You measure your work in CIR basis points and revenue lift, not commits.
• Embedded and fearless. Comfortable sitting with operators, learning a domain cold, and owning the result in front of business leaders.
• Bias to ship. Production over PowerPoint. Fast iteration, tight feedback loops.
• Excellent communicator who can translate technical work for non-technical stakeholders. Bilingual Vietnamese/English is a strong plus.