DHI Solutions - Generative AI Engineer - Deep Learning

Job Title : Gen AI Expert

Location : Gurgaon (WFO)

Experience Level : 4+ years in AI/ML development (with hands-on GenAI exposure). Must have real time projects exposure.

About The Role

Key Responsibilities

• Design, develop, and optimize Transformer-based models (e.g., BERT, GPT, T5) for a range of generative tasks such as text summarization, Q&A, content creation, and code generation.
• Implement and experiment with deep learning techniques, especially focusing on batch normalization, dropout, residual connections, and attention mechanisms to improve model training and convergence.
• Build and train contrastive learning frameworks (e.g., SimCLR, CLIP, MoCo) for representation learning and fine-tuning pre-trained models on domain-specific data.
• Develop scalable information retrieval systems, integrating dense retrieval and vector[1]based semantic search using FAISS or similar technologies.
• Optimize model performance for inference using techniques such as quantization, distillation, and hardware-aware tuning (GPU/TPU-based training).
• Collaborate with data scientists and product teams to define and deliver user-focused GenAI solutions.
• Stay updated with state-of-the-art research in generative AI, deep learning, and retrieval-augmented generation (RAG), and incorporate best practices into development.
  

Required Skills And Qualifications

• Strong hands-on experience with Transformer architectures and their practical implementations using frameworks like Hugging Face Transformers, PyTorch, or TensorFlow.
• Deep understanding of deep learning fundamentals, including backpropagation, batch/layer normalization, weight initialization, and regularization.
• Practical experience with contrastive learning paradigms and implementing self supervised learning strategies.
• Solid grasp of information retrieval systems, vector databases, embedding generation, and semantic search methodologies.
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
• Experience with end-to-end model lifecycle : data preprocessing, model design, training, evaluation, and deployment.
• Ability to handle large datasets and distributed training.
• Knowledge of MLOps tools (e.g., MLflow, Weights & Biases) and cloud platforms (AWS/GCP/Azure).
  

Nice To Have

• Experience in Retrieval-Augmented Generation (RAG) pipelines.
• Familiarity with LoRA, PEFT, or other fine-tuning methods for large language models.
• Prior contributions to open-source GenAI projects.
• Experience with multi-modal models (text + image/audio).