Machine Learning Salary In Kerala 2025

 Machine Learning

         

 As of 2025, the average annual salary for a Machine Learning Engineer in Kerala varies depending on the source:

• Glassdoor: Reports an average salary of ₹8,10,000 per year in Kerala.

• Indeed: Indicates an average salary of ₹2,99,795 per year in Kerala.

These discrepancies highlight the variability in reported salaries across different platforms.

In specific cities within Kerala, salary ranges can differ:

• Thiruvananthapuram: Salaries range between ₹3.6 lakhs to ₹12 lakhs per year for professionals with 1 to 4 years of experience.

• Kochi: Salaries range between ₹1.8 lakhs to ₹11 lakhs per year for professionals with 1 to 4 years of experience.

For comparison, the national average salary for a Machine Learning Engineer in India is approximately ₹9,73,618 per year.

These variations can be attributed to factors such as experience, specific skill sets, industry demand, and the size of the employing organization.

A Machine Learning (ML) Engineer is responsible for designing, developing, and deploying machine learning models to solve real-world problems. They work with data scientists, software engineers, and business stakeholders to build intelligent systems that leverage data and AI.

Key Responsibilities of a Machine Learning Engineer:

1. Data Collection & Preprocessing

• Gather and clean data from various sources (databases, APIs, web scraping, etc.).

• Handle missing data, outliers, and inconsistencies.

• Perform feature engineering and selection for model optimization.

2. Model Development & Training

• Design and develop machine learning models for classification, regression, clustering, NLP, and more.

• Train and fine-tune models using frameworks like TensorFlow, PyTorch, Scikit-learn, or XGBoost.

• Experiment with different algorithms to improve accuracy and performance.

3. Model Evaluation & Optimization

• Evaluate models using metrics such as accuracy, precision, recall, RMSE, and F1-score.

• Implement model validation techniques to prevent overfitting and underfitting.

4. Deployment & Integration

• Deploy models into production using cloud services (AWS, Google Cloud, Azure) or on-premise infrastructure.

• Work with MLOps tools (Docker, Kubernetes, CI/CD pipelines) for model scalability.

• Convert models into APIs or microservices using Flask, FastAPI, or TensorFlow Serving.

5. Continuous Monitoring & Maintenance

• Monitor model performance in real-world environments and retrain when necessary.

• Handle concept drift and data drift to maintain model accuracy over time.

• Improve model efficiency for faster inference and lower computational costs.

6. Research & Innovation

• Experiment with new techniques, architectures, and tools (e.g., Transformers, GANs, AutoML).

• Collaborate with data scientists and researchers to improve existing solutions.

7. Collaboration & Documentation

• Work with cross-functional teams, including data engineers, software developers, and product managers.

• Document processes, experiments, and findings for future reference.

• Communicate insights and results to non-technical stakeholders.

Optional Responsibilities (Based on Role)

• Natural Language Processing (NLP) – If working in text analytics, develop chatbots, sentiment analysis, and translation models.

• Computer Vision – Work on image processing, object detection, and facial recognition models.

• Reinforcement Learning – Implement RL algorithms for automation and decision-making systems.

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