Lead AI Engineer

Job Description

Leads the development of the AI platform and ensures the scalability of the platform accommodates a wide spectrum of ML models and integration of new AI advancements as APIs. Leads the architectural design, development, and implementation of sophisticated tools and frameworks that elevate ML experimentation and deployment, ensuring that the platform delivers high-end AI functionalities. Architects and orchestrates the management of graphics processing unit and central processing unit resources to enhance the performance of AI models while maximizing cost efficiency and resource utilization. Mentors and guides junior team members to ensure a seamless integration of AI models into the platform. Leads the creation and management of robust data movement strategies and pipelines, optimizing data flows to meet the requirements of intensive AI model training and inference processes. Leads initiatives for continuous improvement by evaluating platform performance analytics and gathering user feedback. Leads cross-functional teams, fostering a collaborative environment that merges deep technical knowledge with strategic business insights and a keen understanding of user needs. Champions the development and enforcement of rigorous security protocols and governance measures for the AI platform, ensuring data integrity and adherence to industry standards and best practices. Required Qualifications: Bachelors degree in science, technology, engineering, math, or related field (or equivalent work experience in lieu of degree) and 6 years of experience executing and deploying data science, ML, and DL (fewer years may be accepted with a Masters or Doctorate degree), ML platform engineering, MLOps tools, and ML frameworks 6 years of programming experience in Java and/or Python 4 years of experience working with cross-functional partners Preferred Qualifications: Masters degree in science, technology, engineering, math, statistics, physics, economics, data science, information science, or quantitative analytics 5 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.