As a highly skilled and innovative Generative AI Engineer at GlobalLogic, you will be part of a dynamic AI team, where you will work on designing, building, and deploying cutting-edge Generative AI and Deep Learning solutions. Your role will involve working across the full AI lifecycle, from LLM training and fine-tuning to building advanced NLP systems and production-grade RAG pipelines. If you are passionate about pushing the boundaries of artificial intelligence and solving real-world problems at scale, GlobalLogic wants to hear from you. **Key Responsibilities:**
Design, develop, and deploy advanced Generative AI solutions using state-of-the-art models and frameworks. - Train, fine-tune, and optimize Large Language Models (LLMs) for domain-specific use cases. - Implement and enhance Retrieval-Augmented Generation (RAG) pipelines with vector databases. - Build robust Deep Learning and Neural Network architectures for scalable AI systems. - Apply NLP techniques including text classification, sentiment analysis, NER, and embedding generation. - Manage the end-to-end AI lifecycle: data preprocessing, embedding preparation, model training, evaluation, deployment, and monitoring. - Develop clean, efficient, and well-documented Python code. - Collaborate with cross-functional teams (Product, Engineering, Data Science) to deliver high-impact AI capabilities. - Stay current with the latest AI advancements, tools, and research. **Qualifications Required:**
2-5 years of experience in building and deploying Generative AI and Deep Learning models in production environments. - Strong understanding of Neural Network fundamentals, including model architectures, optimization techniques, and performance evaluation. - In-depth knowledge of Large Language Model (LLM) architectures, training methodologies, and fine-tuning techniques such as LoRA and QLoRA. - Practical experience in Natural Language Processing (NLP) and embedding techniques, including Word2Vec, GloVe, and Sentence Transformers. - Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines and working with vector databases such as Pinecone and ChromaDB. - Strong proficiency in Python and core data science libraries/frameworks, including TensorFlow, PyTorch, Hugging Face, and LangChain. - Experience with MLOps practices and tools such as Docker, Kubernetes, and MLflow for model deployment, monitoring, and lifecycle management. - Familiarity with cloud platforms including AWS, GCP, and Azure for scalable AI solution deployment. - Experience integrating APIs from major AI providers such as OpenAI and Google. - Excellent problem-solving skills with the ability to work both independently and collaboratively in cross-functional teams. - Contributions to open-source AI/ML projects or a strong project portfolio demonstrating practical expertise. - Published research in AI, NLP, or Machine Learning is a plus. As an employee at GlobalLogic, you will benefit from a culture of caring, commitment to learning and development, interesting and meaningful work, balance and flexibility, and being part of a high-trust organization that values integrity. GlobalLogic is a trusted digital engineering partner to the world's largest and most forward-thinking companies, helping create innovative digital products and experiences since 2000. As a highly skilled and innovative Generative AI Engineer at GlobalLogic, you will be part of a dynamic AI team, where you will work on designing, building, and deploying cutting-edge Generative AI and Deep Learning solutions. Your role will involve working across the full AI lifecycle, from LLM training and fine-tuning to building advanced NLP systems and production-grade RAG pipelines. If you are passionate about pushing the boundaries of artificial intelligence and solving real-world problems at scale, GlobalLogic wants to hear from you. **Key Responsibilities:**
Design, develop, and deploy advanced Generative AI solutions using state-of-the-art models and frameworks. - Train, fine-tune, and optimize Large Language Models (LLMs) for domain-specific use cases. - Implement and enhance Retrieval-Augmented Generation (RAG) pipelines with vector databases. - Build robust Deep Learning and Neural Network architectures for scalable AI systems. - Apply NLP techniques including text classification, sentiment analysis, NER, and embedding generation. - Manage the end-to-end AI lifecycle: data preprocessing, embedding preparation, model training, evaluation, deployment, and monitoring. - Develop clean, efficient, and well-documented Python code. - Collaborate with cross-functional teams (Product, Engineering, Data Science) to deliver high-impact AI capabilities. - Stay current with the latest AI advancements, tools, and research. **Qualifications Required:**
2-5 years of experience in building and deploying Generative AI and Deep Learning models in production environments. - Strong understanding of Neural Networ