# Deep Learning Engineer
**Company:** Hiringhood
**Employment Type:** Full Time
**Experience:** 3–6 Years
**No. of Positions:** 1
**CTC:** ₹22–40 LPA
**Location:** Hyderabad, Telangana
**Qualification:** Bachelor's Degree
**Office Timings:** 09:00 – 18:00
**Notice Period:** Immediate Joiner
**Onsite Feasibility:** Yes
## Job Description
We are seeking a skilled Deep Learning Engineer to design, train, and deploy advanced neural network architectures. This role goes beyond fine-tuning pretrained models, requiring hands-on experience building deep learning models from scratch. You will work on high-impact problems across areas such as computer vision, NLP, and multimodal AI, contributing directly to the development of innovative AI products.
## Must Have Skills
* Design, implement, and train deep neural networks (e.g., CNNs, RNNs, Transformers, Diffusion Models).
* Python (P1), PyTorch (P2) or TensorFlow (P2).
* Neural network theory – backpropagation, activation functions, initialization, and optimization.
* Training on large datasets, including distributed training and GPU acceleration.
* Debugging training instabilities, overfitting, or mode collapse.
* 3+ years of experience in training deep learning models end-to-end, beyond pre-trained fine-tuning.
* Strong programming skills in Python, with hands-on experience in PyTorch or TensorFlow.
* Solid understanding of neural network theory: backpropagation, activation functions, initialization, and optimization.
* Experience training on large datasets, including distributed training and GPU acceleration.
* Ability to design data pipelines and manage the model lifecycle in production environments.
* Strong intuition for debugging training instabilities, overfitting, or mode collapse.
## Good to Have Skills
* Productionizing models using tools like ONNX, TensorRT, or quantization.
* Knowledge of LLMs, diffusion models, or custom neural architectures.
* Contributions to open-source ML projects or published research papers.
* Experience working with multimodal inputs (e.g., vision + text, audio + text).
## Key Responsibilities
* Design, implement, and train deep neural networks (e.g., CNNs, RNNs, Transformers, Diffusion Models) from scratch.
* Select and optimize loss functions, training algorithms, regularization techniques, and data augmentation strategies.
* Analyze model performance using metrics, visualizations, ablations, and interpretability tools.
* Write clean, modular, and efficient code for research experiments and production deployment.
* Collaborate with ML researchers, data engineers, and product teams to bring models into production.
* Stay updated with the latest AI/ML research and assess innovations for adoption.
* Conduct internal benchmarks and document model development processes.
## Required Qualifications
* 3+ years of experience in training deep learning models end-to-end, beyond pre-trained fine-tuning.
* Strong programming skills in Python, with hands-on experience in PyTorch or TensorFlow.
* Solid understanding of neural network theory: backpropagation, activation functions, initialization, and optimization.
* Experience training on large datasets, including distributed training and GPU acceleration.
* Ability to design data pipelines and manage the model lifecycle in production environments.
* Strong intuition for debugging training instabilities, overfitting, or mode collapse.
## Preferred Qualifications
* Experience in productionizing models using tools like ONNX, TensorRT, or quantization.
* Knowledge of LLMs, diffusion models, or custom neural architectures.
* Contributions to open-source ML projects or published research papers.
* Experience working with multimodal inputs (e.g., vision + text, audio + text).
## Soft Skills
* Strong analytical and problem-solving abilities.
* Ability to work independently and collaboratively in research-driven environments.
* Passion for keeping up with the latest AI/ML trends and breakthroughs.
* Clear communication skills for sharing technical insights with cross-functional teams.
Pay: ₹2,200,000.00 - ₹4,000,000.00 per year
Work Location: In person