I’m Bing Bai, an AI Solutions Engineer / FDE with three years of enterprise AI experience and approximately RMB 5 million in cumulative project delivery value. I turn business requirements into working systems, spanning requirements discovery, solution design, model validation, backend development, system integration and deployment. I coordinate technical implementation and client delivery: all projects under my responsibility have been delivered as agreed, with a 100% renewal rate among projects that have reached their renewal cycle.
My technical background spans computer vision, multimodal and language model applications, RAG, model compression, and cloud and edge deployment. Working with limited data, offline environments, and compute and budget constraints, I use experiments and evaluation to establish a feasible path, connect models to databases, tools and business workflows, and resolve the engineering challenges between a demo and practical use.
I value explainable delivery and considered costs. Visualizations, comparative experiments and failure cases help clients understand the evidence behind results, the system’s limits and where human review is needed. I also account for model calls, compute, deployment and maintenance when making technical choices, so both performance and spending can be assessed against evidence.
I aim to grow alongside projects and clients. I communicate delivery risks, help clients use systems through training, documentation and feedback, and turn field issues into the next round of improvements. My goal is to coordinate models, agents, tools and people so AI becomes a production system that keeps running and creating value.
What I bring
Turn business problems into deliverable solutions
Start with requirements, data and field constraints. Define goals, inputs, workflows, outputs, scope and acceptance criteria before implementing models and systems.
Make results explainable and verifiable
Use visualizations, evaluation results and failure cases to show the evidence behind decisions, the system’s limits and where people need to participate.
Include cost in technical decisions
Compare quality, latency, API costs, compute and maintenance effort. Use evidence to decide whether to adopt, adjust, invest further or stop a technical approach.
Grow alongside projects and clients
Share risks, learn from feedback and document methods during delivery. Training, documentation and retrospectives improve client capability and the next delivery.
Deliver explainable, cost-conscious AI production systems that keep running and improving.
This is the direction I am working toward: from completing individual AI tasks to designing human–AI collaboration, then coordinating models, agents, tools, databases and automation. People retain responsibility for goals, judgment and resource allocation, while systems handle suitable execution tasks and improve through evaluation, feedback and human review.
How I work
Make complex problems understandable and testable, then turn what works into reusable methods.
Simplify the problem
Separate signal from noise and retain the minimum conditions that determine the outcome.
Clarify who needs to do what and what a useful result looks like, then distinguish essential inputs and constraints from details that can wait.
Use analogies
Borrow structures that have worked in other domains, turning a large leap into two smaller steps.
Map new requirements to familiar retrieval, classification, control or workflow problems. Validate reusable parts before addressing differences between contexts.
Reframe the question
When progress stalls, check whether the question itself is narrowing the options.
Replace “How do we improve model accuracy?” with “How do we make this business decision more reliable?” Consider data, workflows, human review and system design together.
Problem decomposition
Break complex problems into testable parts and understand their dependencies.
Work through goals, inputs, user flow, outputs, scope, acceptance criteria and iteration, then define each module’s responsibilities and interfaces.
Work backward
Start with an acceptable outcome and infer the conditions needed to achieve it.
Define acceptance checks, evidence visible to users and affordable costs first. Work backward to data requirements, system capabilities, deployment conditions and the smallest useful experiment.
Induce and generalize
Derive patterns from concrete work, then test whether those patterns transfer to new settings.
Turn experience into evaluation sets, standards, troubleshooting procedures and reusable components. Recheck assumptions before applying a method elsewhere, and revise it using new results.
Career and research
Enterprise IT and data foundations
Early work in data management and technical support at a financial institution developed my experience with SQL, data validation, access workflows and enterprise systems.
AI research and edge engineering
Master’s research and industry collaboration explored activity recognition, sensor selection and model compression, testing algorithm choices against memory, runtime and field constraints.
Enterprise AI delivery and technical leadership
Working in English across several industries, I progressively took on model and backend development, client deployment, solution evaluation, task planning and delivery coordination.
Certification
Alibaba Cloud LLM Certification (ACP)
Alibaba Cloud Certified Professional — LLM
View certificate (opens in a new tab)Education and languages
- Tokyo Institute of Technology · Master of Engineering (Artificial Intelligence)
- Sun Yat-sen University · Bachelor’s degree in Software Engineering
