Focus : Building and Deploying Scalable AI Systems
Goal : Create Robust, Scalable, and Productive 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

📌 Senior AI Engineer (Secunderabad)
🏢 ValueMomentum
📍 Secunderabad