Deep Learning Solutions Architect

Overview:

We are seeking a highly skilled and innovative Solutions Architect to lead the end-to-end deployment of deep learning models for computer vision applications. The ideal candidate will have strong expertise in optimizing deep learning models and building seamless, scalable, and efficient deployment pipelines, culminating in a user-friendly, one-click installation solution.

Key Responsibilities:

1. Model Optimization:
   • Optimize deep learning models for performance, accuracy, and speed tailored to computer vision tasks.
   • Employ techniques like quantization, pruning, and knowledge distillation to ensure efficient inference.
   • Adapt models to run efficiently on a range of platforms, including edge devices, GPUs, and cloud environments.
2. Architecture Design:
   • Design robust, scalable, and modular architectures for model deployment.
   • Develop APIs and services to facilitate integration with existing systems.
   • Architect solutions that support real-time or batch inference requirements.
3. Deployment Engineering:
   • Implement deployment pipelines that automate model packaging, versioning, and testing.
   • Design solutions for multi-platform compatibility (Linux, Windows, edge devices).
   • Ensure security, compliance, and fault tolerance in deployed systems.
4. One-Click Installation:
   • Build intuitive one-click installation packages, simplifying setup for end-users.
   • Develop deployment scripts and containerized solutions using tools like Docker and Kubernetes.
   • Integrate hardware and software dependencies into deployment packages.
5. Cross-functional Collaboration:
   • Work closely with data scientists, ML engineers, and DevOps teams to ensure seamless deployment.
   • Collaborate with clients to understand requirements and adapt solutions to their needs.
   • Provide guidance on hardware selection and infrastructure for optimal model performance.
6. Performance Monitoring and Maintenance:
   • Implement systems for real-time monitoring of model performance in production.
   • Troubleshoot and resolve issues related to deployment and inference.
   • Design feedback loops to facilitate continuous improvement of deployed solutions.
7. Documentation and Support:
   • Develop comprehensive documentation for deployment pipelines and user instructions.
   • Provide training and support to clients and teams on the deployment process.

Key Qualifications:

• Education:
  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Experience:
  • 5+ years of experience in machine learning model deployment, specifically in computer vision.
  • Proven experience with deep learning frameworks like TensorFlow, PyTorch, ONNX, or similar.
  • Strong background in containerization (Docker) and orchestration (Kubernetes).
• Skills:
  • Expertise in model optimization techniques (quantization, pruning, etc.).
  • Proficiency in scripting languages like Python and deployment tools like Flask, FastAPI, or TensorRT.
  • Experience with CI/CD pipelines and cloud platforms (AWS, Azure, GCP).
  • Strong understanding of hardware accelerators (GPUs, TPUs, edge devices like NVIDIA Jetson).
• Soft Skills:
  • Excellent problem-solving and analytical skills.
  • Strong communication skills to convey technical concepts to diverse stakeholders.
  • Ability to work in a fast-paced, collaborative environment.

Preferred Qualifications:

• Knowledge of hardware-software co-design for computer vision workloads.
• Experience with edge deployment using frameworks like NVIDIA DeepStream or OpenVINO.

Familiarity with monitoring tools like Prometheus and Grafana