Machine Learning Engineer - Generative AI (Hyderabad)

Join our pioneering Core-LLM platform team in Hyderabad, India, as we push the boundaries of Generative AI. We are dedicated to developing robust, scalable, and safe machine learning models, including LLMs, SLMs, Large Reasoning Models (LRMs), and SRMs, that power ServiceNow's cutting-edge products and features.

Responsibilities:

• Design, develop, and evaluate end-to-end machine learning solutions, with a strong focus on large language models (LLMs), combining engineering rigor and research depth.
• Lead the development of Proofs of Concept (PoCs) and applied research prototypes to explore novel AI capabilities, model interpretability, and safety strategies.
• Conduct cutting-edge experiments to assess model behavior, 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.

Qualifications:

• 2+ 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.
• 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.
• 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.
• Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization.
• Experience in using AI Productivity tools such as Cursor, Windsurf, etc. is a plus.
• Practical experience deploying models using inference platforms like Triton, ONNX in production environments is a plus.