Job
Description
As an AI Engineer, you will be responsible for designing, training, and fine-tuning models for various tasks such as image classification, segmentation, object detection, text classification, summarization, and generation. You will also build and integrate generative AI solutions using Large Language Models (LLMs) like GPT, LLaMA, and BERT. Your role will involve optimizing and deploying AI models in scalable, GPU-accelerated environments while collaborating with cross-functional teams to ensure robust model deployment and monitoring. Additionally, you will translate business requirements into AI solutions and continuously enhance model performance in terms of accuracy, latency, and resource efficiency. Key Responsibilities: - Design, train, and fine-tune models for tasks including image classification, segmentation, and object detection, as well as text classification, summarization, and generation. - Build and integrate generative AI solutions using LLMs like GPT, LLaMA, and BERT. - Optimize and deploy AI models in scalable, GPU-accelerated environments. - Collaborate with engineering, DevOps, and IT teams to ensure robust model deployment and monitoring. - Translate business requirements into AI solutions through close interaction with product and customer teams. - Continuously improve model performance with respect to accuracy, latency, and resource efficiency. Qualifications Required: - Strong understanding of deep learning, computer vision, and NLP fundamentals. - Hands-on experience with CNNs, RNNs/LSTMs, attention mechanisms, transformers, and generative models. - Proficiency in PyTorch or TensorFlow. - Experience working with LLM platforms such as Hugging Face, OpenAI, or similar. - Exposure to prompt engineering, fine-tuning, or embedding-based retrieval. - Familiarity with GPU-based training workflows, distributed computing, or MLOps tools is a plus. - Solid programming skills in Python and comfort with Git, Docker, or related tools. If you have experience with vector databases like FAISS or Pinecone, knowledge of ML lifecycle tools such as MLflow, Weights & Biases, or DVC, and exposure to cloud platforms like AWS, GCP, or Azure for model training/deployment, it would be considered a plus. As an AI Engineer, you will be responsible for designing, training, and fine-tuning models for various tasks such as image classification, segmentation, object detection, text classification, summarization, and generation. You will also build and integrate generative AI solutions using Large Language Models (LLMs) like GPT, LLaMA, and BERT. Your role will involve optimizing and deploying AI models in scalable, GPU-accelerated environments while collaborating with cross-functional teams to ensure robust model deployment and monitoring. Additionally, you will translate business requirements into AI solutions and continuously enhance model performance in terms of accuracy, latency, and resource efficiency. Key Responsibilities: - Design, train, and fine-tune models for tasks including image classification, segmentation, and object detection, as well as text classification, summarization, and generation. - Build and integrate generative AI solutions using LLMs like GPT, LLaMA, and BERT. - Optimize and deploy AI models in scalable, GPU-accelerated environments. - Collaborate with engineering, DevOps, and IT teams to ensure robust model deployment and monitoring. - Translate business requirements into AI solutions through close interaction with product and customer teams. - Continuously improve model performance with respect to accuracy, latency, and resource efficiency. Qualifications Required: - Strong understanding of deep learning, computer vision, and NLP fundamentals. - Hands-on experience with CNNs, RNNs/LSTMs, attention mechanisms, transformers, and generative models. - Proficiency in PyTorch or TensorFlow. - Experience working with LLM platforms such as Hugging Face, OpenAI, or similar. - Exposure to prompt engineering, fine-tuning, or embedding-based retrieval. - Familiarity with GPU-based training workflows, distributed computing, or MLOps tools is a plus. - Solid programming skills in Python and comfort with Git, Docker, or related tools. If you have experience with vector databases like FAISS or Pinecone, knowledge of ML lifecycle tools such as MLflow, Weights & Biases, or DVC, and exposure to cloud platforms like AWS, GCP, or Azure for model training/deployment, it would be considered a plus.
