Build and integrate solutions using LLMs and Generative AI frameworks.
Apply prompt engineering techniques to optimize LLM outputs for various use cases.
Fine-tune pre-trained models to align with domain-specific requirements.
Work with Azure OpenAI for deploying, scaling, and managing LLM workloads.
Understand and implement deep learning architectures like CNNs and RNNs as required for multimodal and NLP tasks.
Write clean and modular code to support experimentation and productization of AI models.
Collaborate with data scientists, engineers, and business teams to convert problem statements into technical solutions.
Stay updated with the latest trends and research in LLMs, deep learning, and Generative AI.
Strong logical thinking and problem-solving abilities.
Proficiency in Python and ML/NLP libraries (e.g., PyTorch, TensorFlow, Hugging Face).
Hands-on experience with Generative AI and LLMs (e.g., GPT, LLaMA, Mistral).
Knowledge of prompt engineering strategies for improving model interaction.
Experience with Azure OpenAI services (deployment, model management).
Understanding of deep learning models like CNN, RNN, and attention mechanisms.
Exposure to vector databases, LangChain, or RAG pipelines.
Experience in model fine-tuning, quantization, or training from scratch.
Familiarity with ML workflows, data preprocessing, and evaluation metrics.
Job Classification
Industry: IT Services & Consulting Functional Area / Department: Data Science & Analytics Role Category: Data Science & Analytics - Other Role: Data Science & Analytics - Other Employement Type: Full time