Ai Ml Engineer / AIML

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.
• Strong 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).
• Strong understanding of cloud platforms and modern data architectures (e.g., AWS, GCP, Azure).