Senior Machine Learning Engineer

Calix provides the cloud, software platforms, systems and services required for communications service providers to simplify their businesses, excite their subscribers and grow their value.

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.