AI / ML Engineer – Intern / Fresher / Experienced

Location : Vadodara

Type : Full-time / Internship

Duration (for interns) : Minimum 3 months

Stipend / CTC : Based on experience and role

About Gururo

Gururo is an industry leader in practical, career-transforming education. With a mission to empower professionals and students through real-world skills, we specialize in project management, leadership development, and emerging technologies. Join our fast-paced, mission-driven team and work on AI / ML-powered platforms that impact thousands globally.

Who Can Apply?

Interns : Final-year students or recent graduates from Computer Science, Data Science, or related fields, with a strong passion for AI / ML.

Freshers : 0–1 years of experience with academic or internship exposure to machine learning projects.

Experienced Professionals : 1+ years of hands-on experience in AI / ML roles with a demonstrated portfolio or GitHub contributions.

Key Responsibilities

Design and develop machine learning models and AI systems for real-world applications

Clean, preprocess, and analyze large datasets using Python and relevant libraries

Build and deploy ML pipelines using tools like Scikit-learn, TensorFlow, PyTorch

Work on NLP, Computer Vision, or Recommendation Systems based on project needs

Evaluate models with appropriate metrics and fine-tune for performance

Collaborate with product, engineering, and design teams to integrate AI into platforms

Maintain documentation, participate in model audits, and ensure ethical AI practices

Use version control (Git), cloud deployment (AWS, GCP), and experiment tracking tools (MLflow, Weights & Biases)

Must-Have Skills

Strong Python programming skills

Hands-on experience with one or more ML frameworks (Scikit-learn, TensorFlow, or PyTorch)

Good understanding of core ML algorithms (classification, regression, clustering, etc.)

Familiarity with data wrangling libraries (Pandas, NumPy) and visualization (Matplotlib, Seaborn)

Experience working with Jupyter Notebooks and version control (Git)

Basic understanding of model evaluation techniques and metrics

Good to Have (Optional)

Exposure to deep learning, NLP (transformers, BERT), or computer vision (OpenCV, CNNs)

Experience with cloud ML platforms (AWS SageMaker, GCP AI Platform, etc.)

Familiarity with Docker, APIs, and ML model deployment workflows

Knowledge of MLOps tools and CI / CD for AI systems

Kaggle profile, published papers, or open-source contributions

What You’ll Gain

Work on real-world AI / ML problems in the fast-growing EdTech space

Learn from senior data scientists and engineers in a mentorship-driven environment

Certificate of Internship / Experience & Letter of Recommendation (for interns)

Opportunities to lead research-driven AI initiatives at scale

Flexible work hours and performance-based growth opportunities

End-to-end exposure — from data collection to model deployment in production