AI/ML

Posted on July 23, 2025

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Job Description

  • Role Summary: Looking for one data scientist engineer with Strong experience in AI/ML, Data collection preprocessing, estimation, Architecture creation
  • Experience: 6-10 years.
  • Responsibility:
  • Model Development: Design and implement ML models to tackle complex business challenges.
  • Data Preprocessing: Clean, preprocess, and analyze large datasets for meaningful insights and model features.
  • Model Training: Train and fine-tune ML models using various techniques including deep learning and ensemble methods.
  • Evaluation and Optimization: Assess model performance, optimize for accuracy, efficiency, and scalability.
  • Deployment: Deploy ML models in production, monitor performance for reliability.
  • Collaboration: Work with data scientists, engineers, and stakeholders to integrate ML solutions.
  • Research: Stay updated on ML/AI advancements, contribute to internal knowledge.
  • Documentation: Maintain comprehensive documentation for all ML models and processes.
  • Qualification - Bachelor's or master�s in computer science, Machine Learning, Data Science, or a related field and must be experience of 6-10 years.
  • Desirable Skills:
  • o Must Have
  • 1. Experience in timeseries forecasting, regression Model, Classification Model
  • 2. Python , R, Data analysis
  • 3. Large size data handling with Panda , Numpy and Matplot Lib
  • 4. Version Control: Git or any other
  • 5. ML Framework: Hands on exp in Tensorflow, Pytorch, Scikit-Learn, Keras
  • 6. Good knowledge on Cloud platform and ( AWS/ AZure/ GCP), Docker kubernetis
  • 7. Model Selection, evaluation, Deployment, Data collection and preprocessing, Feature engineering
  • Estimation
  • o Good to Have
  • � Experience with Big Data and analytics using technologies like Hadoop, Spark, etc.
  • � Additional experience or knowledge in AI/ML technologies beyond the mentioned frameworks.
  • � BFSI and banking domain

Required Skills

1. experience in timeseries forecasting regression model classification model 2. python r data analysis 3. large size data handling with panda numpy and matplot lib 4. version control: git or any other 5. ml framework: hands on exp in tensorflow pytorch scikit-learn keras 6. good knowledge on cloud platform and ( aws/ azure/ gcp) docker kubernetis 7. model selection evaluation deployment data collection and preprocessing feature engineering