Role & responsibilities
Support the development and enhancement of scalable and optimized artificial intelligence (AI)/machine learning (ML) models.
Deployment and development of various algorithms for anomaly detection and event classification
Able to handle multiple customer specific POCs (Model creation, deployment, tuning).
Build algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data to deploy AI/ML models.
Perform experimentation to test the performance of deployed models, and identify and resolve bugs that arise in the process.
Perform UAT to measure accuracy of model and generate performance metrics
Select appropriate datasets and data representation methods for analysis. Develop scalable data pipelines to extract, transform, load and integrate unstructured data from various sources.
Develop codes to package the AI/ML models for scaling, develop AI/ML development pipeline and infrastructure
Test the operation and performance of the deployed models, identify bugs during deployment and create bug fixes to address issues
Engage in code reviews to improve AI/ML models and support continuous improvement of AI solutions
Prepare documentation to outline data sources,



models and algorithms used and developed.
Develop and optimize CNN-based models for event detection and classification using spectrograms and other signal-derived image formats.
Apply computer vision techniques to identify spatial and temporal patterns in DAS data visualizations.
Use deep learning architectures (e.g., CNN, RNN, LSTM) to model sequential and spatial aspects

Preferred candidate profile
5+ years of hands-on experience in AI/ML engineering, with real-world deployment of models and pipelines.
Robust expertise in Python
Proven experience building and deploying LLM-based systems, particularly those using retrieval-augmented generation (RAG).
Solid understanding of vector databases, embeddings, and semantic search architecture.
Strong skills in data engineering: ETL pipelines, data cleaning, transformation, and large-scale processing.
Experience in building REST APIs, containerizing services with Docker, and deploying to cloud infrastructure (preferably Azure or AWS).
Solid understanding of cloud platforms and contemporary data architectures (e.g., AWS, GCP, Azure).

📌 Ai Ml Engineer / Aiml Hyderabad (India)
🏢 Dreamz Hire
📍 India