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