Intern | ML Engineer | Mumbai | Information Technology

Job Description: Intern – Machine Learning Engineer

Role Overview:

As a Machine Learning Engineer – Design, implement, and deploy state-of-the-art generative models and machine learning algorithms. 

Key Responsibilities:

Model Development: Design, develop and deploy generative models such as GANs (Generative Adversarial Networks), transformers, and large language models.

Research & Innovation: Stay on top of the latest trends in AI, including research papers, open-source advancements, and breakthroughs in generative models and deep learning. Contribute to the development of proprietary algorithms and techniques.

Data Preparation & Preprocessing: Collaborate with data engineers to prepare, clean, and process large datasets suitable for training generative models.

Training & Optimization: Train large-scale models using advanced machine learning techniques. Optimize models for efficiency, scalability, and performance in real-world applications.

Deployment & Monitoring: Deploy models into production environments, and set up monitoring and testing to ensure model accuracy, performance, and robustness over time.

Collaboration: Work closely with cross-functional teams, including software engineers, data scientists, and product managers, to integrate AI/ML solutions into our products and services.

Code Quality & Documentation: Write clean, efficient, and maintainable code. Document processes, algorithms, and model decisions to ensure transparency and ease of collaboration.

Continuous Improvement: Analyze model performance, identify areas for improvement, and iteratively refine models based on performance metrics and real-world feedback.

Qualifications:

Education: Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, Data Science, Engineering, or a related field.

Experience:

Proven experience in fine-tuning models like GANs, VAEs, Transformers, or other generative architectures.

Strong programming skills in Python (preferred).

Familiarity with machine learning libraries and frameworks like TensorFlow, PyTorch, Hugging Face and Keras.

Skills & Knowledge:

Experience in fine-tuning pre-trained models (e.g., GPT-3, BERT, etc.).

Strong understanding of deep learning algorithms, architectures, and optimization techniques.

Hands-on experience with large-scale model training, GPU computing, and cloud platforms like Azure.

Familiarity with NLP, computer vision, or other relevant fields of generative AI.

Understanding of model interpretability, explainability, and ethical AI considerations.

Experience in fine-tuning pre-trained models (e.g., GPT-3, BERT, etc.).

Good To Have

Familiarity with MLOps practices, version control (Git), and CI/CD pipelines.

Contribution to open-source AI projects or published research papers in AI/ML.