Predective System developement

About the Role

Predictive System Development Engineer

Key Responsibilities

• Design, develop, and deploy

  predictive models

  using machine learning and statistical techniques.
• Work with large, structured, and unstructured datasets to extract meaningful insights.
  
• Implement data pipelines for

  data collection, cleaning, transformation, and feature engineering

  .
• Collaborate with cross-functional teams to understand business requirements and translate them into predictive solutions.
  
• Build

  end-to-end predictive systems

  that integrate with existing platforms or software applications.
• Validate, monitor, and optimize model performance to ensure accuracy, scalability, and reliability.
  
• Develop dashboards, reports, or APIs to make predictive insights accessible to stakeholders.
  
• Stay updated with the latest trends, algorithms, and technologies in

  machine learning, AI, and predictive analytics

  .
• Document methodology, workflows, and system architecture for reproducibility and knowledge sharing.
  

Required Skills & Qualifications

• Bachelors or Master’s degree in

  Computer Science, Data Science, Statistics, Mathematics, or related field

  .
• 2–6 years of experience in

  predictive modeling, machine learning, or AI system development

  .
• Strong programming skills in

  Python, R, or Java

  , and experience with machine learning libraries (

  scikit-learn, TensorFlow, PyTorch, XGBoost, etc.

  ).
• Experience with

  data preprocessing, feature engineering, model evaluation, and hyperparameter tuning

  .
• Hands-on experience with

  data storage and processing

  technologies (SQL, NoSQL, Hadoop, Spark).
• Knowledge of

  statistical modeling, time series forecasting, regression, classification, and clustering

  .
• Familiarity with

  cloud platforms

  (AWS, Azure, GCP) and deployment frameworks.
• Strong problem-solving, analytical, and communication skills.
  

Nice-to-Have

• Experience with

  real-time predictive systems and streaming data

  .
• Familiarity with

  MLOps, model deployment, and monitoring frameworks

  .
• Knowledge of

  optimization algorithms, reinforcement learning, or deep learning techniques

  .
• Exposure to

  business intelligence tools

  (Tableau, Power BI, Looker) for predictive insights visualization.
• Publications or contributions in

  predictive analytics or AI projects

  .