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Silent Infotech
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AI Engineer

Job Expired: This job listing has expired.

AI Engineer

Silent Infotech

5 - 10 years

7 - 11 Lacs

Ahmedabad

Posted:8 months ago| Platform:

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Skills Required

Computer science Training deep learning GCP Machine learning Programming Deployment Open source Predictive analytics Python

Work Mode

Work from Office

Job Type

Full Time

Job Description

We are seeking an AI Engineer with expertise in training machine learning models and a strong understanding of the toolchain. The ideal candidate will have hands-on experience in fine-tuning, optimizing, and deploying models, ensuring efficient training pipelines for real-world applications.

About the Role:

Location:

Ahmedabad

Experience:

5+ years of total industry experience, with at least 2+ years in AI/ML model training.

Education preference:

Bachelor s degree in Computer Science, Engineering, or a related field.

Key Responsibilities:

Rain and fine-tune ML/DL models for various AI applications (e.g., NLP, CV, or Predictive Analytics).

Implement and optimize training pipelines, ensuring efficiency and scalability.

Work with toolchains (e.g., TensorFlow, PyTorch, Hugging Face, ONNX, MLflow, Weights Biases).

Leverage data preprocessing, augmentation, and feature engineering techniques to improve model performance.

Optimize models for deployment (quantization, pruning, distillation, etc.).

Collaborate with Data Scientists, MLOps engineers, and software developers to integrate AI solutions into production systems.

Stay updated with the latest AI research, open-source models, and best practices.

Required Skills Qualifications:

5+ years of total industry experience, with at least 2+ years in AI/ML model training.

Hands-on experience with AI training pipelines and frameworks (PyTorch, TensorFlow, JAX, etc.).

Experience in model fine-tuning, hyperparameter optimization, and dataset curation.

Strong programming skills in Python (NumPy, Pandas, Scikit-learn, etc.).

Familiarity with GPU acceleration (CUDA, TensorRT) and cloud-based training (AWS, GCP, Azure).

Experience with MLOps tools like MLflow, Weights Biases, or Kubeflow is a plus.

Solid understanding of deep learning architectures (CNNs, Transformers, LLMs, etc.).

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