Senior Machine Learning Engineer

Senior Machine Learning Engineer - Hyderabad

As a Senior Machine Learning Engineer at ServiceNow in Hyderabad, you will be instrumental in designing, developing, and evaluating end-to-end machine learning solutions, with a significant focus on Large Language Models (LLMs). This role requires a blend of engineering rigor and deep research understanding.

What you will do:

• 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, leveraging prompt engineering, few-shot learning, and retrieval-augmented techniques.
• Collaborate with product, research, and engineering teams to publish white papers, present at 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, focusing on transparency, robustness, and compliance.

Key qualifications:

• 5+ years of experience in machine learning, deep learning, or data science, with a proven track record in 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.
• Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization.
• 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, labeling 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.
• Experience in using AI Productivity tools such as Cursor, Windsurf, etc. is a plus.