Associate AI/ML Engineer

At Upview, we are not just building products; we are building a culture of continuous learning, creativity, and empowerment. We want to inspire you to think differently, embrace new technologies, and push the limits of what is possible. Join us on this exciting journey, and together we will shape the future of the digital landscape.

Requirements

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 2-3 years of hands-on experience developing and deploying AI and ML models.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with sparse data representations and techniques, such as handling high-dimensional data in NLP or recommendation systems.
• Practical experience integrating AI models with IoT systems, including knowledge of IoT protocols (e.g., MQTT, CoAP) and platforms (e.g., AWS IoT, Azure IoT Hub).
• Demonstrated ability to implement real-time inferencing for large datasets, ensuring low-latency and high-throughput performance.
• Familiarity with edge computing and deploying models on resource-constrained devices.
• Knowledge of version control (Git) and collaborative platforms (GitHub, GitLab).
• Strong problem-solving skills and the ability to optimize AI systems for performance and scalability.
• Certifications or specialized training in AI/ML (e.g., Coursera, Udacity, or vendor-specific programs) are a plus.

About the Role

• Design and develop AI and ML models that leverage sparse vector representations for efficient processing of high-dimensional data.
• Implement and optimize deep learning models for applications such as NLP, computer vision, or time-series analysis from IoT devices.
• Optimizing for both memory-bound and compute-bound operations.
• Reasoning about register pressure, shared-memory usage and GPU utilization through tools such as Nsight and removing bottlenecks.
• Being familiar with the latest and the most effective techniques in optimizing inference and training workloads.
• Integrate AI solutions with IoT systems, enabling real-time data processing and decision-making at the edge or in the cloud.
• Develop and deploy real-time inferencing pipelines capable of handling large-scale datasets with minimal latency.
• Collaborate with cross-functional teams to ensure seamless integration of AI components into broader IoT ecosystems.
• Monitor and enhance model performance in production, using MLOps tools to track and improve system reliability.
• Stay current with emerging trends in AI, ML, and IoT to drive innovation and maintain a competitive edge.