Senior AI Vision Systems Developer

2 days ago

Singapore beBeeVision Full time
Job Title: AI Vision Architect
About the Role
We're building cutting-edge Computer Vision technology, automating data curation, model tuning, and deployment. Our mission is to bridge research with production software by empowering engineers with intelligent tools that scale.
Key Responsibilities
  • Design and implement scalable agent-driven systems for Computer Vision MLOps workflows (training, deployment, evaluation)
  • Develop robust APIs and backend services for agent orchestration, memory management, and cross-component communication
  • Integrate vector databases, data quality validation, and semantic search to power intelligent agent behavior
  • Own the end-to-end software lifecycle: from development to testing, CI/CD automation, and cloud deployment
  • Build for reliability and maintainability: logging, monitoring, and exception handling are part of your design thinking
  • Optimize performance and cost in storage and compute, especially for vision-specific workloads
  • Work with researchers and domain experts to translate state-of-the-art ideas into working code
Requirements
  • 5+ years of hands-on software engineering experience, with at least 2–3 years building AI/ML systems
  • Strong Python development skills, including writing testable, production-grade code
  • Proven experience with:
    • Designing and deploying CI/CD pipelines for ML systems
    • Containerization and orchestration (Docker, Kubernetes)
    • Cloud infrastructure (AWS, GCP, or Azure)
    • API development using frameworks like FastAPI or Flask
    • Database technologies: SQL/NoSQL and vector databases (e.g., FAISS, Weaviate)
  • Familiarity with AI agent frameworks such as LangChain, AutoGen, CrewAI
  • Solid understanding of Computer Vision workflows, including image classification, detection, and tracking
  • Comfortable with software engineering best practices: version control, code reviews, documentation, modular design
Nice-to-Have Skills
  • Demonstrated experience through personal projects, prototypes, or open-source contributions in AI, CV, or agents
  • Hands-on with ML frameworks like PyTorch or TensorFlow
  • Exposure to ML observability and performance monitoring tools (e.g., Grafana, Datadog, Prometheus)
  • Experience building simple user-facing interfaces for demos or internal tools (e.g., with Gradio or Streamlit)
  • Familiarity with unit testing frameworks (e.g., Pytest) and static analysis tools
  • Prior experience in cross-functional collaboration, especially with researchers or product engineers
  • Knowledge of LLM integration for autonomous agent systems

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