Job Description

Job Summary

We are seeking an experienced Deep Learning Engineer with 2–5 years of hands-on experience in designing, training, and deploying deep learning models. The ideal candidate should have strong expertise in neural networks and real-world AI applications.

Key Skills & Technologies

Deep Learning, Neural Networks, CNN, RNN, LSTM, Transformers, Generative AI, Python, TensorFlow, PyTorch, Keras, NLP, Computer Vision, OpenCV, Model Optimization, Model Deployment, MLOps, Docker, Kubernetes, REST APIs, Cloud (AWS/GCP/Azure), LLMs

Job Description (JD)

• Design, build, and deploy scalable deep learning models across multiple domains
• Work on applications in computer vision, NLP, and generative AI
• Optimize model performance for accuracy, speed, and efficiency
• Handle large-scale datasets including data preprocessing and feature engineering
• Integrate AI models into production systems and real-world applications
• Ensure scalability, reliability, and maintainability of deployed solutions
• Monitor model performance and implement continuous improvements
• Collaborate with cross-functional teams to align AI solutions with business goals
• Evaluate and select appropriate frameworks, tools, and architectures
• Stay updated with advancements in deep learning, generative AI, and emerging technologies

Roles & Responsibilities

• Design and implement advanced deep learning architectures 
• Develop and deploy models for NLP, Computer Vision, and Generative AI 
• Optimize model performance and scalability 
• Handle large datasets and perform feature engineering 
• Deploy models using APIs and cloud platforms 
• Collaborate with cross-functional teams 
• Mentor junior engineers and review code 
• Stay updated with latest AI research and advancements 

Eligibility Criteria

• Bachelor’s or Master’s degree in Computer Science, AI, or related field 
• 2–5 years of hands-on experience in deep learning 
• Strong proficiency in Python and frameworks (TensorFlow, PyTorch) 
• Experience in real-world AI model deployment 
• Strong understanding of neural network architectures 
• Knowledge of software engineering best practices