Senior Machine Learning Engineer

This role is based in Bangalore with hybrid working model.

Our Products Team is growing and we’re looking for a highly skilled Senior Machine Learning Engineer to join our cutting-edge Generative AI project. In this role, you will play a key part in designing, developing, and deploying advanced AI models focused on content generation, natural language understanding, and creative data synthesis. You will work alongside a team of data scientists, software engineers, and AI researchers to build systems that push the boundaries of what generative AI can achieve.

Key Responsibilities:

• Design and Build ML Models: Develop and implement advanced machine learning models (including deep learning architectures) for generative tasks, such as text generation, image synthesis, and other creative AI applications.
• Optimize Generative AI Models: Enhance the performance of models like GPT, VAEs, GANs, and Transformer architectures for content generation, making them faster, more efficient, and scalable.
• Data Preparation and Management: Preprocess large datasets, handle data augmentation, and create synthetic data to train generative models, ensuring high-quality inputs for model training.
• Model Training and Fine-tuning: Train large-scale generative models and fine-tune pre-trained models (e.g., GPT, BERT, DALL-E) for specific use cases, using techniques like transfer learning, prompt engineering, and reinforcement learning.
• Performance Evaluation: Evaluate models’ performance using various metrics (accuracy, perplexity, FID, BLEU, etc.), and iterate on the model design to achieve better outcomes.
• Collaboration with Research and Engineering Teams: Collaborate with cross-functional teams including AI researchers, data scientists, and software developers to integrate ML models into production systems.
• Experimentation and Prototyping: Conduct research experiments and build prototypes to test new algorithms, architectures, and generative techniques, translating research breakthroughs into real-world applications.
• Deployment and Scaling: Deploy generative models into production environments, ensuring scalability, reliability, and robustness of AI solutions in real-world applications.
• Stay Up-to-Date with Trends: Continuously explore the latest trends and advancements in generative AI, machine learning, and deep learning to keep our systems at the cutting edge of innovation.

Qualifications:

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field.
• 3-5+ years focus on Machine Learning.
• 5+ years overall software engineering in production
• Proven experience with generative AI models such as GPT, VAEs, GANs, or Transformer architectures.
• Strong hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
• Expertise in Python and libraries such as NumPy, Pandas, Scikit-learn.
• Experience with Natural Language Processing (NLP), image generation, or multimodal models.
• Familiarity with training and fine-tuning large-scale models (e.g., GPT, BERT, DALL-E).
• Knowledge of cloud platforms (AWS, GCP, Azure) and ML ops pipelines (e.g., Docker, Kubernetes) for deploying machine learning models.
• Strong background in data manipulation, data engineering, and working with large datasets.
• Strong coding experience in Python, Java, Go, C/C++, R  (prefer Python)
• Good data skills – SQL, Pandas, exposure to various SQL and no SQL data bases.
• Solid development experience with dev cycle on Testing and CICD.
• Strong problem-solving abilities and attention to detail.
• Excellent collaboration and communication skills to work effectively within a multidisciplinary team.
• Proactive approach to learning and exploring new AI technologies.

Preferred Skills:

• Experience with Reinforcement Learning or Self-Supervised Learning in generative contexts.
• Familiarity with distributed training and high-performance computing (HPC) for scaling large models.
• Contributions to AI research communities or participation in AI challenges and open-source projects.
• Tools: Linux, git, Jupyter, IDE, ML frameworks: Tensorflow, Pytorch, Keras, Scikit-learn.
• GenAI: prompt engineering, RAG pipeline, Vector/Graph DB, evaluation frameworks, model safety and governance.