Sr AI Engineer

Job Description

Scales the platform for high performance and integrates new AI capabilities as application programming interfaces to ensure the platform remains adaptable and efficient in hosting a variety of ML models. Designs, develops, and implements tools and frameworks that support ML experimentation and deployment. Manages graphics processing unit (GPU) and central processing unit (CPU) resources to optimize the execution of AI models to ensure the platform runs efficiently, balancing performance with cost-effectiveness. Works closely with data scientists to integrate AI models smoothly into platform. Creates and manages efficient data movement and pipelines for the AI platform to operate smoothly. Optimizes data flows to support the demands of high-velocity AI model training and inference. Analyzes platform performance metrics and user feedback to drive continuous improvement initiatives. Utilizes insights to guide platform enhancements, ensuring the AI platform remains at the forefront of technological advancements and user satisfaction. Collaborates effectively with diverse teams, integrating technical expertise with business insights and user needs. Implements security protocols and governance measure for AI platform, ensuring data integrity and compliance with industry standards and best practices. Years of Experience: 3 to 5 Required Minimum Qualifications : Bachelors degree in science, technology, engineering, math, or related field (or equivalent work experience in lieu of degree) and 4 years of experience in ML platform engineering, data, and MLOps tools, and ML frameworks Preferred Qualifications: Masters Degree in science, technology, engineering, math, statistics, physics, economics, data science, information science, or quantitative analytics 3 years of experience working with GPU and CPU infrastructure, optimizing ML models for performance 3 years of programming language experience. Experience working with Continuous Integration/Continuous Deployment tools 3 years of experience working with continuous integration/continuous deployment tools 3 years of experience in defining technical requirements and performing high-level design for complex solutions 3 years of experience in SQL and NoSQL databases, Hadoop ecosystem, Druid, Trino, Big Query, Google Vertex AI Skill Set Required Python Primary language for AI/ML model development, especially for deep learning frameworks. Strong knowledge of multiprocessing and multithreading in Python or C++(Optional) for handling high-throughput systems. Docker, Kubernetes (for handling large-scale deployments). Knowledge of microservices architectures, message brokers like Kafka or RabbitMQ. Experience with serving models using TensorFlow Serving, Torch Serve, or Fast API. - Model deployment MLflow, Kubeflow, or DVC for managing the lifecycle of models. - model management Git , Jenkins, CI for continuous integration and deployment. Deep Learning Frameworks and Image processing: Pytorch, tensorflow, - Building and deploying computer vision models. OpenCV, scikit-image, or PIL for basic image processing. should aware of these framework and hands on experience Nvidia deep learning stacks and model serving frame works - good to have Should have work experience with real-time or offline video processing pipeline with Deep learning application development Optimization & Performance Tuning - Ability to improve AI model performance and reduce latency. Models Awareness : Computer Vision - Knowledge of CNNs, object detection, and image/video processing techniques. Must aware of object detection, classification and segmentation models ( Yolo, any open source model) Optimizing models for edge deployment using TensorRT, OpenVINO, or ONNX. Knowledge of tracking (SORT, DeepSORT) and semantic segmentation techniques. Awareness is enough Secondary Skills (desired) Good to have : Techniques for reducing inference time, including model pruning, quantization, and optimized inference with TensorRT or ONNX Runtime. Prometheus, Grafana, and ELK Stack for monitoring and logging AI/ML models in production. Experience with large-scale data management tools such as Hadoop, Spark, and distributed databases Other programming languages JS, UI backend services related knowledge.