Engineer

About the Role

GlobalLogic is seeking a skilled and innovative Generative AI Engineer to join our dynamic AI team in Gurgaon/Gurugram, India. You will be responsible for designing, building, and deploying cutting-edge Generative AI and Deep Learning solutions. This role involves the full AI lifecycle, from LLM training and fine-tuning to developing advanced NLP systems and production-grade RAG pipelines. If you are passionate about advancing artificial intelligence and solving complex real-world challenges at scale, we encourage you to apply.

Key Responsibilities

• Design, develop, and deploy advanced Generative AI solutions utilizing state-of-the-art models and frameworks.
• Train, fine-tune, and optimize Large Language Models (LLMs) for specific domain applications.
• Implement and enhance Retrieval-Augmented Generation (RAG) pipelines integrated with vector databases.
• Build robust Deep Learning and Neural Network architectures for scalable AI systems.
• Apply Natural Language Processing (NLP) techniques such as text classification, sentiment analysis, Named Entity Recognition (NER), and embedding generation.
• Manage the complete AI lifecycle: data preprocessing, embedding preparation, model training, evaluation, deployment, and monitoring.
• Write clean, efficient, and well-documented Python code.
• Collaborate effectively with cross-functional teams, including Product, Engineering, and Data Science, to deliver impactful AI capabilities.
• Maintain up-to-date knowledge of the latest advancements, tools, and research in artificial intelligence.

Required Qualifications

• 2-5 years of experience in building and deploying Generative AI and Deep Learning models in production environments.
• Solid 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 (e.g., LoRA, QLoRA).
• Practical experience in Natural Language Processing (NLP) and embedding techniques (e.g., Word2Vec, GloVe, Sentence Transformers).
• Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines and working with vector databases (e.g., Pinecone, ChromaDB).
• Strong proficiency in Python and core data science libraries/frameworks like TensorFlow, PyTorch, Hugging Face, and LangChain.
• Experience with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow) for model deployment, monitoring, and lifecycle management.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for deploying scalable AI solutions.
• Experience integrating APIs from major AI providers like OpenAI and Google.
• Excellent problem-solving abilities, with capacity for both independent work and collaboration within cross-functional teams.
• Demonstrated expertise through contributions to open-source AI/ML projects or a strong project portfolio.
• Published research in AI, NLP, or Machine Learning is considered a plus.