Machine Learning Engineer
New
J
JobgetherSecurity & IT
Based in IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- At least 2 years of experience
- Required Skills
- PythonMachine LearningPyTorchSoftware Engineering
Requirements
- At least 2 years of experience in Machine Learning Engineering, Applied AI, or a closely related field.
- Demonstrated experience building, deploying, and maintaining machine learning systems in production environments.
- Hands-on experience fine-tuning transformer-based models and/or large language models.
- Strong Python programming and software engineering skills.
- Experience with modern machine learning frameworks such as PyTorch and Hugging Face.
- Practical knowledge of model optimization techniques and experience improving inference efficiency at scale.
- Solid understanding of machine learning model evaluation, experimentation, and performance measurement.
- Experience working with data pipelines and datasets used for supervised machine learning.
- Understanding of distributed training and scalable machine learning infrastructure.
- Experience deploying ML models in cloud-based or containerized environments.
- Strong understanding of production software engineering principles, including reliability, scalability, maintainability, and monitoring.
Responsibilities
- Design, train, fine-tune, and evaluate machine learning models for security detection use cases.
- Build and deploy lightweight, high-performance models optimized for low latency, high throughput, low inference cost, and operational reliability.
- Develop and maintain fine-tuning pipelines for large language models and smaller transformer-based architectures.
- Experiment with advanced model optimization techniques, including knowledge distillation, quantization, pruning, retrieval-augmented generation, and parameter-efficient fine-tuning.
- Build scalable machine learning infrastructure and production-grade inference pipelines.
- Partner with security researchers to translate detection logic into ML-powered production systems.
- Monitor deployed models and identify opportunities to improve robustness, reliability, and performance.
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