About Us
We build enterprise AI systems that connect company knowledge, AI agents, tools, and business workflows.
Our product, Counso.AI, helps enterprises move beyond standalone AI assistants to secure, governed, and repeatable AI workflows, with support for enterprise identity, permission-aware access, auditability, and flexible deployment. We work closely with financial institutions, professional services firms, and other enterprises with complex data, security, and workflow requirements.
The Role
We are looking for an AI Engineer to design, build, and improve the AI systems that power Counso.AI. You will work across agents, retrieval, tool use, memory, workflow orchestration, evaluation, model integration, and production reliability.
This is a hands-on engineering role. You will work closely with product and platform teams to turn emerging AI capabilities into dependable product features for real enterprise workflows.
What You Will Do
- Design and build production-grade AI agents and agentic workflows.
- Develop RAG, retrieval, document-processing, and enterprise knowledge systems.
- Build tool-use, memory, orchestration, approval, and human-in-the-loop capabilities.
- Integrate and evaluate models from multiple providers, including model selection, routing, prompting, and execution strategies.
- Build structured-output, function-calling, and multi-step workflows for enterprise use cases.
- Develop evaluation systems for task success, accuracy, reliability, latency, and cost.
- Design guardrails, validation, fallback, failure-recovery, tracing, and observability mechanisms.
- Diagnose failures across models, prompts, retrieval, tools, data, application logic, and infrastructure.
- Build reusable AI components and platform capabilities that support multiple products and customer deployments.
- Work with product and engineering teams to translate business requirements into practical system designs.
What We Are Looking For
- Strong Python programming skills and solid software engineering fundamentals.
- Hands-on experience with LLM applications, AI agents, tool calling, workflow orchestration, or RAG.
- Experience with APIs, databases, testing, debugging, version control, and production development.
- Understanding of prompting, structured outputs, function calling, context management, and model evaluation.
- Strong debugging and root-cause analysis skills, with the ability to take an AI feature from experimentation to production.
- Strong written and verbal communication skills in English.
- Proficiency in Mandarin Chinese is required for regular collaboration with Mandarin-speaking stakeholders.
- Bachelor’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, or equivalent practical experience.
Nice to Have
- Experience building production AI applications, agent systems, developer platforms, or ML systems.
- Experience with agent frameworks such as LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, or similar systems.
- Familiarity with MCP, agent memory, evaluation, tracing, observability, and versioning.
- Experience with vector search, retrieval infrastructure, cloud platforms, containers, and CI/CD.
- Understanding of enterprise security concepts such as identity, permissions, secrets, audit logs, and data governance.
- Experience with document-heavy, workflow-heavy, or regulated enterprise applications.
- Open-source contributions, research, technical writing, or substantial deployed AI projects.
Who Should Apply
Candidates with relevant professional experience are preferred. Fresh graduates with strong hands-on AI engineering experience demonstrated through internships, research, open-source contributions, competitions, or substantial deployed projects are also encouraged to apply.
What Success Looks Like
You can take an AI capability from an early experiment to a reliable production system, identify why it fails, and implement practical improvements across the model, prompt, retrieval, tools, data, orchestration, and surrounding application logic.