Senior Machine Learning Engineer

Snow Planet

5 - 7 years

3 - 7 Lacs

hyderabad

Posted:7 hours ago| Platform:

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Skills Required

training deep learning data science focus machine learning deployment research open source product research python

Work Mode

Work from Office

Job Type

Full Time

Job Description

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

Key qualifications:

Experience in using AI Productivity tools such as Cursor, Windsurf, etc. is a plus or nice to have
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.

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