AI ENGINEER

End- to- End Agent Orchestration

Implement context management, memory retrieval, and streaming responses to support real- time conversational agents.
Design, build, and maintain scalable, high- performance microservices that orchestrate autonomous agent workflows: prompt planning, tool invocation, multi- step decision loops, and response generation.
LangGraph & Pipeline Tooling

Leverage LangGraph to define dynamic, graph- based LLM pipelines—authoring nodes, edges, and execution logic for complex agent behaviors (e.g., conditional branching, tool use, external API calls).
MLOps & CI/CD for Agents

Implement continuous integration, canary releases, blue- green deployments, and rollback strategies for both base LLMs and agent orchestration code.
Develop robust infrastructure to automate model training, versioning, testing, and deployment.
Cross- Functional Collaboration

Partner with Product Managers, Business Analysts, and AI Researchers to translate novel agent architectures into production- ready services.
Communicate design trade- offs, performance benchmarks, and roadmap items to both technical and non- technical stakeholders.

Experience & Languages

Hands- on Python expertise in production.
2+ years building backend services for ML/AI products (Chatbot, AI Agent, or Computer Vision).

LLM & Conversational AI

Deep familiarity with at least one LLM framework (e.g., Hugging Face Transformers, OpenAI API).
Proven ability to architect prompt management, session/state persistence, fallback strategies, and safe- completion policies.
Systems & Scalability
LangGraph Expertise: Demonstrable track record designing and deploying LangGraph pipelines for multi- stage conversational or agent workflows.
Strong understanding of data structures, algorithms, network I/O, concurrency models, and distributed systems.
Experience with containerized, low- latency services using Kubernetes, Docker, and gRPC/REST.
Software Engineering Best Practices
Competence in unit/integration testing, version control (Git), code reviews, and common design patterns

Preferred Skills

AI Agent & Chatbot Development: Prior work building and deploying autonomous AI Agents or advanced Chatbot platforms in production.
Model Serving Frameworks: Hands- on with two or more inference stacks (e.g., TorchScript/TorchServe, TensorRT, NVIDIA Triton Inference Server).
MLOps Platform: Experience with Kubeflow (Pipelines, Katib), MLflow, or similar for end- to- end ML lifecycle automation.
High- Throughput Inference: Experience developing secure, low- latency, high- concurrency ML or web backend services—ideally supporting thousands of concurrent sessions.
Container Orchestration: Deep familiarity with Docker, Kubernetes (including autoscaling and service meshes), and secure multi- tenant deployments.

Great opportunities to get self promotion and recognitions coming from member’s contributions
Lunch allowance
Insurance coverage.
Receive training sessions on skill development, business knowledge, etc...
Young, dynamic, creative and comfortable working environment
Performance evaluation once/year
Birthday party & personalised gifts
12 days’ annual leave/ year
13th month salary, Bonus twice a year up to 05 months

Cập nhật gần nhất lúc: 2025-08-18 20:15:02