AI engineer - NLP & Computer Vision

Role Overview: As a Machine Intelligence Specialist for our client in the technology, information, and media industry based in Bengaluru, you will be an integral part of the forward-thinking engineering team. Your primary focus will be on developing intelligent systems that utilize advanced natural language processing (NLP), computer vision, and machine learning technologies to automate processes and tasks efficiently. Key Responsibilities: - Design and Develop Intelligent Systems: - Create robust machine learning models for tasks in NLP and computer vision domains, such as text summarization, named entity recognition, image classification, OCR, and object detection. - Deep Learning Engineering: - Build and optimize models using TensorFlow, Keras, or PyTorch, incorporating neural architectures like CNNs, RNNs, LSTMs, and Transformers. - Collaborative Innovation: - Partner with product, data, and engineering teams to ensure AI development aligns with real business needs, facilitating seamless integration of intelligence into products and workflows. - Cloud-Native Deployment: - Implement and scale ML models on cloud platforms like AWS, Azure, or GCP using native services and containerization tools such as Docker and Kubernetes. - AI at Scale: - Drive performance tuning, data preprocessing, A/B testing, and continuous training to maintain accuracy and production reliability. - Model Lifecycle Ownership: - Implement MLOps best practices, including CI/CD pipelines for ML, model versioning, and monitoring using tools like MLflow or SageMaker. - Ethical & Transparent AI: - Prioritize explainability, fairness, and compliance throughout the model development lifecycle. Qualification Required: - Essential Qualifications: - 3+ years of hands-on experience in designing, training, and deploying machine learning models. - Strong proficiency in Python and ML libraries such as TensorFlow, Keras, OpenCV, scikit-learn, and Hugging Face Transformers. - Experience in NLP and computer vision use cases and toolkits. - Proven ability to deploy models using cloud-native AI tools on AWS, Azure, or GCP. - Familiarity with containerization (Docker) and orchestration (Kubernetes). - Solid foundation in mathematics, statistics, and deep learning algorithms. - Excellent communication skills and a collaborative mindset. - Bonus Points For: - Experience with MLOps workflows and tools like MLflow, TFX, or Kubeflow. - Exposure to edge AI or streaming inference systems. - Understanding of responsible AI principles and data governance.,