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ML Lead - Python

ML Lead - Python

STL Digital Consulting
Fresher
Not Disclosed
Early Applicant
  • Posted a month ago
  • Be among the first 10 applicants

Job Description

Role Description

Role Purpose: Key Accountabilities: Preferred Experience and Qualification:

Other Details

  • Driven by innovation, design, plan and implement Data & (Gen)AI architectures including the associated roadmaps, where you work together with Enterprise Architects and Senior Management up to C-Level on the customer side
  • Design and set up experiments for collecting, labeling analyzing data of different kinds of events from fiber based sensor solutions
  • Assess the effectiveness and accuracy of data sources and data gathering techniques
  • Develop automation methods for faster labeling of collected data
  • Create architectural approaches for AI software and hardware integration
  • Define AI solution objectives and ensure alignment with business outcomes
  • Implement best practices for AI testing, deployment, and maintenance
  • Develop Machine Learning and Deep Learning methods for event detection and classification
  • Develop efficient frameworks for training and evaluating the event detection/classification models
  • Track the innovations in AI/ML technologies and algorithms/processing/ computation and its implementation to develop next generation fiber sensing solution
  • Review and Optimize existing code/algorithm
  • Create systematic plans to run the experiments to fine-tune the event detection/classification models
  • Plan the tasks and provide estimates and timelines
  • Support marketing/sales team with application notes, white papers and presentations
  • Contribute to simulation model development and data analysis of various sensor solutions
  • Design, 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.

More Info

Key Skills

Data Gen AI architectures

AI testing deployment and maintenance

AI software and hardware integration

computer vision techniques

LSTM

automation methods

fiber based sensor solutions

event detection and classification

data gathering techniques