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Associate Machine Learning - job post

Corrosion Intel
Noida, Uttar Pradesh
₹20,000 - ₹25,000 a month

Job details

Pay

  • ₹20,000 - ₹25,000 a month

Job type

  • Full-time

Location

Noida, Uttar Pradesh

Full job description

Responsibilities

  • Understand, clean, and process diverse industrial and business datasets (time-series, sensor data, operational logs, tabular and semi-structured data).
  • Design, train, and evaluate machine learning and deep learning models for real-world business and industrial use cases.
  • Build deployable ML/DL pipelines, including data preprocessing, model inference, and performance monitoring.
  • Support rapid prototyping of models and experiments to validate ideas and iterate quickly.
  • Collaborate closely with a multidisciplinary team comprising domain experts, physicists, chemical engineers, and software developers.
  • Work with the software development team to integrate models into production-grade enterprise AI platforms.
  • Petrochemical assets (refineries, storage tanks, pipelines, process units)
  • Metal and mineral processing assets
  • Assist in model validation, documentation, and performance benchmarking.

Qualifications

  • BTech, MCA or equivalent degree with strong foundations in computer science.
  • Solid understanding of machine learning and deep learning fundamentals.
  • Hands-on experience with Python for ML development (NumPy, Pandas, PyTorch / TensorFlow / scikit-learn).
  • Working knowledge of C++ for performance-critical components or integration is preferred.
  • Ability to prototype and iterate models quickly is mandatory.
  • Basic familiarity with data pipelines, model training workflows, and deployment concepts.
  • Strong problem-solving skills and willingness to learn domain-specific industrial systems.

Job Type: Full-time

Pay: ₹20,000.00 - ₹25,000.00 per month

Application Question(s):

  • Are you willing to relocate to NOIDA
  • Can you commit an employment of 1 year or more

Experience:

  • Machine learning: 1 year (Required)

Work Location: In person

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