
AI Vision Architect
4 days ago
Building the Future of Computer Vision
We are revolutionizing computer vision development by creating intelligent tools that accelerate the delivery of real-world AI systems.
About the Role
A senior software engineer with strong AI/ML experience is needed to design, build, and ship a Computer Vision platform powered by AI agents. The ideal candidate will play a key role in delivering scalable, production-grade systems that automate and accelerate ML workflows.
Key Responsibilities
- Design and implement agent-driven systems to automate Computer Vision MLOps workflows.
- 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, incorporating logging, monitoring, and exception handling.
- Optimize performance and cost in storage and compute, particularly for vision-specific workloads.
- Collaborate with researchers and domain experts to translate state-of-the-art ideas into working code.
Requirements
- 8+ 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 CI/CD pipelines, containerization, cloud infrastructure, API development, database technologies, and AI agent frameworks.
- Solid understanding of Computer Vision workflows, including image classification, detection, and tracking.
- Comfortable with software engineering best practices, including version control, code reviews, documentation, and modular design.
Bonus Skills
- Demonstrated experience through personal projects or open-source contributions in AI, CV, or agents.
- Hands-on experience with ML frameworks like PyTorch or TensorFlow.
- Exposure to ML observability and performance monitoring tools.
- Experience building simple user-facing interfaces for demos or internal tools.
- Familiarity with unit testing frameworks and static analysis tools.
- Prior experience in cross-functional collaboration, especially with researchers or product engineers.
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