The Role

📍We are seeking a self-driven and motivated Machine Learning Engineer with a specialized focus on building robust infrastructure tailored for handling large-scale inference workloads for AI and ML models. You will spearhead the design, development, and optimization of high-performance AI/ML systems capable of supporting real-time and batch processing requirements across diverse domains. You will collaborate closely with cross-functional teams to architect and implement cutting-edge inference pipelines and infrastructure, ensuring the reliability, efficiency, and scalability of our machine learning model deployments. Your responsibilities will include: 📍Design and implement scalable and efficient machine learning inference infrastructure and architecture to support real-time and batch processing requirements. 📍Develop deployment pipelines and tools for deploying machine learning models into production environments, including containerization (e.g., Docker) and orchestration (e.g., Kubernetes). 📍Optimize model inference performance and resource utilization through techniques such as model quantization, pruning, and acceleration (e.g., GPU/TPU utilization, model caching). 📍Continuously evaluate and improve the performance of machine learning models and infrastructure through experimentation and optimization techniques.

Qualifications

📍Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field 📍Strong background in machine learning, deep learning, and statistical modeling with hands-on experience in developing and deploying ML infrastructure 📍Proficiency in programming languages such as Python, Java, C++, Go 📍Extensive experience with popular ML frameworks (e.g., TensorFlow, PyTorch, HuggingFace) and technologies (e.g. CUDA, ONNX) 📍Experience as a software engineer with deep understanding of algorithms and data structures 📍Have familiarity with the latest AI and ML research and working knowledge of how these systems are efficiently implemented.