Machine Learning and AI Developer

2 - 5 years

2 - 4 Lacs

Posted:1 month ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Maddisoft has the following immediate opportunity.Send in your resume ASAP along with LinkedIn profile without which applications will not be considered. All qualified Indian citizens are encouraged to apply.

Role: Machine Learning and AI Developer

Location: Pune, Hyderabad

Job Summary:

Machine Learning and AI Developer

Key Responsibilities:

  • Assist in designing, developing, testing, and deploying machine learning models.
  • Collect, clean, and preprocess structured and unstructured data.
  • Contribute to feature engineering, model training, evaluation, and optimization.
  • Implement AI algorithms using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Integrate ML models into production systems using REST APIs or cloud platforms.
  • Monitor model performance and assist in retraining and updating models as needed.
  • Work closely with data scientists, engineers, and product teams to deliver end-to-end ML solutions.
  • Document code, models, and processes clearly and effectively.

Required Qualifications:

  • Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
  • 24 years of experience working with ML/AI projects.
  • Solid understanding of ML concepts such as supervised/unsupervised learning, regression, classification, clustering, etc.
  • Proficiency in Python and ML libraries such as Scikit-learn, Pandas, NumPy.
  • Familiarity with deep learning frameworks like TensorFlow, Keras, or PyTorch.
  • Experience with data visualization tools (e.g., Matplotlib, Seaborn).
  • Basic knowledge of version control tools like Git.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Exposure to MLOps practices (e.g., model monitoring, CI/CD for ML).
  • Understanding of NLP, computer vision, or time-series forecasting.
  • Experience working with large datasets and databases (SQL/NoSQL).
  • Familiarity with containerization (Docker) and APIs for model deployment.

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