Generative AI Engineer

Role Overview: As a Generative AI Engineer at GlobalLogic, you will be responsible for designing, developing, and implementing advanced Generative AI solutions using state-of-the-art models and techniques. You will work on training, fine-tuning, and evaluating Large Language Models (LLMs) for specific use cases, integrating Retrieval-Augmented Generation (RAG) systems, and applying expertise in Deep Learning and Neural Networks to build robust and scalable AI systems. Additionally, you will utilize Natural Language Processing (NLP) techniques for various tasks and collaborate with cross-functional teams to deliver high-impact AI features. Key Responsibilities: - Design, develop, and implement advanced Generative AI solutions using state-of-the-art models and techniques. - Train, fine-tune, and evaluate Large Language Models (LLMs) for specific use cases, ensuring optimal performance and accuracy. - Develop and integrate Retrieval-Augmented Generation (RAG) systems to enhance model responses with external knowledge bases. - Apply expertise in Deep Learning and Neural Networks to build robust and scalable AI systems. - Utilize Natural Language Processing (NLP) techniques for tasks such as text classification, sentiment analysis, and named entity recognition. - Manage the full lifecycle of AI models, including embedding preparation, data preprocessing, model deployment, and monitoring. - Write clean, efficient, and well-documented code, primarily in Python. - Collaborate with cross-functional teams to deliver high-impact AI features. - Stay current with the latest advancements in the field. Qualification Required: - Bachelors or Masters degree in Computer Science, AI, Machine Learning, or a related field. - Proven hands-on experience in building and deploying Gen AI and Deep Learning models. - Strong proficiency in Python and its core data science libraries (e.g., TensorFlow, PyTorch, Hugging Face, LangChain). - In-depth knowledge of LLM architecture, LLM training methodologies, and fine-tuning techniques (e.g., LoRA, QLoRA). - Solid understanding of Neural Network fundamentals. - Experience with NLP, including text processing and embedding preparation (e.g., Word2Vec, GloVe, sentence transformers). - Practical experience implementing RAG pipelines and vector databases (e.g., Pinecone, ChromaDB). - Familiarity with APIs from major AI providers like OpenAI, Google. - Strong problem-solving skills and the ability to work independently and in a team environment. About GlobalLogic: GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner known for collaborating with clients to transform businesses and redefine industries through intelligent products, platforms, and services. Since 2000, GlobalLogic has been at the forefront of the digital revolution, creating innovative digital products and experiences for the world's largest companies. At GlobalLogic, you will have the opportunity to work on exciting projects in industries like High-Tech, communication, media, healthcare, retail, and telecom, collaborating with a diverse team in a collaborative and flexible work environment. Additionally, GlobalLogic offers professional development opportunities, excellent benefits, and fun perks to ensure a rewarding and balanced work experience. Role Overview: As a Generative AI Engineer at GlobalLogic, you will be responsible for designing, developing, and implementing advanced Generative AI solutions using state-of-the-art models and techniques. You will work on training, fine-tuning, and evaluating Large Language Models (LLMs) for specific use cases, integrating Retrieval-Augmented Generation (RAG) systems, and applying expertise in Deep Learning and Neural Networks to build robust and scalable AI systems. Additionally, you will utilize Natural Language Processing (NLP) techniques for various tasks and collaborate with cross-functional teams to deliver high-impact AI features. Key Responsibilities: - Design, develop, and implement advanced Generative AI solutions using state-of-the-art models and techniques. - Train, fine-tune, and evaluate Large Language Models (LLMs) for specific use cases, ensuring optimal performance and accuracy. - Develop and integrate Retrieval-Augmented Generation (RAG) systems to enhance model responses with external knowledge bases. - Apply expertise in Deep Learning and Neural Networks to build robust and scalable AI systems. - Utilize Natural Language Processing (NLP) techniques for tasks such as text classification, sentiment analysis, and named entity recognition. - Manage the full lifecycle of AI models, including embedding preparation, data preprocessing, model deployment, and monitoring. - Write clean, efficient, and well-documented code, primarily in Python. - Collaborate with cross-functional teams to deliver high-impact AI features. - Stay current with the latest advancements in the field. Qualification Required: - Bachelors or Masters degree in Computer Sci