Senior AI Engineer

Focus : Building and Deploying Scalable AI Systems Goal : Create Robust, Scalable, and Efficient AI-Powered Solutions Core Responsibilities : Develop and implement machine learning and deep learning algorithms using Python , TensorFlow , PyTorch , and Scikit-learn Design scalable model training and inference pipelines leveraging Docker , Kubernetes , MLflow , and Airflow Deploy models to cloud platforms such as AWS SageMaker , GCP Vertex AI , and Azure ML Integrate CI/CD workflows using GitHub Actions and infrastructure as code via Terraform Optimize models using ONNX , TensorRT , and techniques like pruning and quantization Manage large-scale data processing with Spark , Kafka , and Hadoop Work with structured and unstructured data stored in SQL , NoSQL , and GraphDBs Tech Stack : Programming : Python ML/DL Frameworks : TensorFlow, PyTorch, Scikit-learn MLOps Tools : MLflow, Airflow, Docker, Kubernetes Cloud Platforms : AWS (SageMaker), GCP (Vertex AI), Azure ML Big Data : Spark, Kafka, Hadoop Databases : SQL, NoSQL, GraphDBs DevOps : CI/CD, GitHub Actions, Terraform Model Optimization : ONNX, TensorRT, Pruning, Quantization