Role: ML Trainer

- Positions: Multiple
- Training Mode: Online / Offline / Both
- Location: Hyderabad for offline delivery; online delivery can be considered irrespective of location
- Engagement: Full-time / Part-time / Contract, depending on requirement

ML Trainer About the Role Welkin Global Academy is looking to onboard multiple ML Trainers to deliver our structured, hands-on Machine Learning program across both online and offline cohorts .

The program covers mathematical foundations, Python, data analysis, classical Machine Learning, model evaluation, and applied ML projects.

We are looking for trainers who can combine strong technical expertise with effective teaching and mentoring skills and help learners build a practical understanding of Machine Learning.

Training Delivery

Trainers may be engaged for:

- Online live training
- Offline classroom training
- Weekend or weekday cohorts
- Full-time training assignments
- Part-time / visiting faculty engagements
- Cohort-based or contractual assignments

Candidates may be considered for online, offline, or both modes , depending on their experience, availability and suitability. Key Responsibilities1. Machine Learning & Mathematical Foundations

- Teach the mathematical foundations required for Machine Learning, including:
- Linear Algebra
- Probability and Statistics
- Basic Calculus
- Vectors, matrices and mathematical operations relevant to ML
- Explain how mathematical concepts translate into practical ML algorithms.
- Build conceptual understanding rather than focusing only on library-based implementation.

1. Python & Data Science Foundations

- Teach Python fundamentals relevant to Machine Learning.
- Cover NumPy and its use in numerical computing.
- Teach data manipulation and analysis using Pandas.
- Introduce data visualization and exploratory data analysis using Matplotlib and Seaborn.

1. Classical Machine Learning

Deliver practical and conceptual instruction covering:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
- K-Nearest Neighbors
- Regression and classification
- Model training and validation
- Feature engineering and feature selection
- Model evaluation and performance metrics
- Cross-validation
- Hyperparameter tuning
- Overfitting and underfitting
- Bias-variance concepts

1. Unsupervised Learning

- Clustering techniques, including K-Means
- Dimensionality reduction
- Principal Component Analysis (PCA)
- Practical applications of unsupervised learning

1. Advanced Classical ML

- Ensemble learning concepts




- Practical implementation of XGBoost
- Algorithm selection and trade-offs
- Model optimisation and performance improvement

1. Projects & Practical Learning

- Conduct hands-on coding exercises and assignments.
- Guide learners through:
- Mini-Project 1
- Mini-Project 2
- Final ML Capstone Project
- Review learner code and provide technical feedback.
- Help learners troubleshoot implementation issues.
- Mentor learners in developing independent ML solutions.
- Ensure projects demonstrate practical understanding rather than simple tutorial replication.

1. Learner Support & Assessment

- Conduct doubt-clearing sessions.
- Evaluate assignments, quizzes and projects.
- Track learner progress and identify knowledge gaps.
- Provide constructive technical feedback.
- Support learners in developing independent problem-solving abilities.

1. Course Content & Continuous Improvement

- Work with the academy team to maintain course notebooks, assignments, exercises and project material.
- Keep content aligned with current Python, Pandas, scikit-learn and XGBoost practices.
- Provide suggestions for improving course delivery and learner outcomes.
- Follow the academy's approved curriculum and learning outcomes while bringing relevant industry examples and practical experience into sessions.

Required Technical Skills
- 3–5+ years of hands-on experience in Machine Learning, Data Science or a closely related field.
- Strong proficiency in Python .
- Strong working knowledge of:
- NumPy
- Pandas
- Matplotlib / Seaborn
- scikit-learn
- XGBoost
- Strong understanding of:
- Statistics
- Probability
- Linear Algebra
- Basic Calculus
- Machine Learning algorithms
- Model evaluation
- Feature engineering
- Model validation
- Hyperparameter tuning
- Experience building and evaluating ML models using real-world datasets.
- Experience working on end-to-end Machine Learning or Data Science projects.

Teaching & Communication Skills Prior teaching, mentoring, corporate training or academic training experience is preferred but not mandatory for candidates with strong industry expertise.

The candidate should be able to:

- Explain technical concepts clearly and systematically.




- Explain the mathematical intuition behind ML algorithms.
- Conduct live coding demonstrations.
- Debug learner code and explain errors.
- Translate complex concepts into practical examples.
- Engage learners through questions, exercises and discussions.
- Teach learners with different levels of technical knowledge.
- Provide effective project guidance and code reviews.

Educational Qualification
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline.
- Master's degree in a relevant field is preferred but not mandatory where robust practical expertise can be demonstrated.

Preferred Experience Experience in any of the following will be an advantage:
- Production Machine Learning
- End-to-end Data Science projects
- ML model deployment
- ML pipelines
- SQL
- Git/GitHub
- Cloud-based ML environments
- Experience mentoring students or professionals
- Experience conducting technical workshops, bootcamps or corporate training
- Experience working with industry datasets and business use cases

Trainer Assessment Shortlisted candidates may be required to conduct a technical teaching demonstration before selection.

The assessment may evaluate

- Technical depth
- Mathematical understanding
- Python and ML implementation skills
- Live coding ability
- Problem-solving approach
- Ability to explain complex concepts simply
- Practical industry understanding
- Classroom communication
- Learner engagement and mentoring ability

Engagement Opportunities We are open to onboarding trainers under different engagement models based on experience, availability and program requirements:
- Full-Time Trainer
- Part-Time Trainer
- Contract Trainer
- Cohort-Based Trainer
- Visiting/Industry Faculty

Training Modes Online | Offline | Hybrid

Trainers may be assigned to one or multiple cohorts depending on their expertise, availability and delivery mode.

Location

Hyderabad, Telangana for offline assignments.

Online trainers may be considered from across India, subject to availability and suitability.

Number of Positions

Multiple positions available.

We are building a trainer pool to support upcoming Machine Learning cohorts across online and offline delivery.

How to Apply

Candidates should share

- Updated CV
- LinkedIn profile
- Portfolio/GitHub profile, if available
- Details of relevant ML projects
- Teaching/training experience, if applicable
- Preferred training mode: Online / Offline / Both
- Preferred engagement: Full time / Part time / Contract / Cohort-Based
- Availability

📌 ML Instructor (Hyderabad)
🏢 Welkin Enterprises
📍 Hyderabad