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