AI ML Team Lead
Posted Apr 24, 2026
Key Responsibilities:
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Model Development & Optimization:
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Design & Implementation:
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Architect and develop end-to-end ML solutions for applications such as predictive analytics, anomaly detection, computer vision, and NLP. - Utilize advanced techniques including deep learning (CNNs, RNNs), reinforcement learning, and generative models (GANs) to address complex challenges. - Optimization:
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Fine-tune model parameters using techniques such as hyperparameter tuning (Grid Search, Bayesian Optimization, Neural Architecture Search). - Optimize models for both accuracy and inference speed to meet real-time processing requirements. - Advanced Data Engineering & Integration:
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Data Pipeline Development:
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Build robust ETL pipelines using libraries like Pandas, NumPy, and PySpark to process large-scale datasets from satellite imagery, IoT sensors, and real-time streams. - Integrate data from diverse sources (APIs, databases, big data platforms like Hadoop and Apache Kafka) to support real-time analytics. - Data Quality & Preprocessing:
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Implement data cleansing, feature engineering, and transformation pipelines to ensure high-quality inputs for ML models. - Research & Innovation:
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Algorithm Research: