AI/ML Internship – PGAGI Consultancy Private Limited | India

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Important Points to Remember

  • Carefully read the internship requirements before applying.

  • Apply only through the official internship application link provided below.

  • Ensure your resume/CV is updated and tailored to the AI/ML Internship role.

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Job Overview




    Category
    Details




    Role
    AI/ML Intern




    Company
    PGAGI Consultancy Private Limited




    Location
    Remote / Pan India




    Work Mode
    Internship (Remote)




    Duration
    3–6 Months (Estimated)




    Experience
    Fresher / Student Intern




    Education
    B.Tech / M.Tech / MCA / Relevant Degrees




    Skills Required
    Python, Machine Learning basics, Data Analysis, Curiosity




About Company

PGAGI Consultancy Private Limited is a consultancy and technology services provider that partners with organisations to deliver innovative solutions across data science, artificial intelligence, machine learning, and business transformation. The company engages interns to work on real-world projects that enhance analytical thinking and technical problem-solving, while cultivating professional skills that accelerate future career success.

Detailed Responsibilities

As an AI/ML Intern, your responsibilities are likely to include:

  • Assisting in data collection, cleaning, and preprocessing tasks.

  • Supporting model development and validation activities.

  • Working with Python libraries for data analysis and machine learning tasks.

  • Creating basic ML models for classification, regression, or clustering.

  • Visualising results and communicating insights.

  • Collaborating with team members to solve data-driven problems.

  • Learning and applying concepts of supervised and unsupervised learning.

  • Assisting in documentation of experiments and workflows.

Eligibility Criteria

  • Current students or recent graduates in Computer Science, Engineering, Data Science, or related fields.

  • Basic knowledge of Python and machine learning fundamentals.

  • Strong analytical mindset with problem-solving abilities.

  • Curiosity for learning new technologies and techniques.

  • Good communication and teamwork skills.

  • Willingness to work remotely and adopt new tools.

Skills Required / Preferred Skills

  • Fundamentals of Python programming.

  • Knowledge of libraries like NumPy, Pandas, Scikit-Learn, TensorFlow or similar.

  • Familiarity with data manipulation and ML workflow.

  • Understanding of basic algorithms and evaluation metrics.

  • Ability to document findings and present insights.

Selection Process




    Stage
    Description




    Application Screening
    Resume evaluation for skills and eligibility




    Technical Assessment
    Machine learning or Python task




    Interview Round
    Discussion on ML concepts and data tasks




    Final Decision
    Selection based on performance and fit

Salary & Benefits

AI/ML internships often come with a stipend that aligns with industry standards for data science internships. Interns may receive experience certificates, mentorship support, hands-on exposure to machine learning projects, and real-world training that enhances professional skills. Remote internships also provide flexibility with learning and working on tasks independently, expanding understanding of ML workflows and analytics tools for future career opportunities.

How to Apply (Step-by-Step)

  1. Visit the official internship application page for the AI/ML Internship.

  2. Sign in or create your account if required.

  3. Fill in personal, educational, and contact details accurately.

  4. Upload your updated resume/CV.

  5. Submit your application and check your email for updates.

👉 Apply here

Resume Tips

  • Keep your resume concise and professional.

  • Highlight any projects or coursework in data science or machine learning.

  • List programming languages and tools you are proficient in.

  • Use clear section headers and a clean layout.

Assessment / Interview Tips

  • Review ML fundamentals such as regression, classification, clustering.

  • Practice basic Python exercises for data handling.

  • Prepare to explain your project experiences or coursework.

  • Show eagerness to learn and engage with real technical problems.

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