Senior Machine Learning Engineer
- Hyderabad, Telangana
- Permanent
- Full-time
- Conduct cutting-edge experiments to assess model behaviour, generalization, and fairness across diverse datasets and use cases.
- Generate and curate synthetic and real-world datasets to optimize model robustness, reliability, and performance.
- Fine-tune and deploy large-scale models, incorporating prompt engineering, few-shot learning, and retrieval-augmented techniques.
- Collaborate cross-functionally with product, research, and engineering teams to publish white papers, participate in conferences, and contribute to open-source or peer-reviewed ML/AI research.
- Define and implement rigorous evaluation protocols, including human-in-the-loop testing, bias detection, and safety metrics.
- Develop CI/CD pipelines and containerized workflows for scalable training, evaluation, and deployment of ML solutions in production.
- Identify risks in AI applications and contribute to responsible AI initiatives, including transparency, robustness, and compliance frameworks.
- Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization
- 5+ years of experience in machine learning, deep learning, or data science, with a track record of applied research or experimentation.
- Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, HuggingFace Transformers, and NumPy.
- Hands-on experience with prompt engineering, model training, evaluation, and optimization for LLMs or foundation models.
- Proven experience in applied research, academic publication, technical blogging, or contributions to open-source ML projects.
- Familiarity with data-centric AI workflows: synthetic data generation, labelling strategies, and dataset versioning tools.
- Deep understanding of AI/ML evaluation strategies, model robustness techniques, and responsible AI practices.
- Practical experience deploying models using inference platforms like Triton, ONNX in production environments.
- Experience working with MLOps stacks: CI/CD, experiment tracking (e.g., MLflow), Docker, Kubernetes, and distributed training frameworks.
- Excellent communication skills with the ability to explain complex ML ideas to non-technical stakeholders and contribute to scientific documentation.