AI Lead Engineer 1

Who We AreAt NTT DATA, we are a global team of over 140,000 professionals, operating in more than 50 countries and serving diverse industries including telecommunications, finance, public sector, healthcare, and more. We are passionate about technology and innovation, and we believe in personalized, merit-based career growth.We are expanding our capabilities in Conversational AI, building intelligent assistants across voice and text channels, web, mobile apps, WhatsApp, Microsoft Teams, using cutting-edge technologies like Generative AI (GPT) and platforms such as Dialogflow CX, BotFramework Composer, Watson Assistant, and Cognigy.What You'll Be DoingDesigning, training, and fine-tuning machine learning and deep learning models.Developing LLM-based solutions (RAG pipelines, chatbots, Q&A assistants, classification/extraction models).Implementing MLOps pipelines for model deployment, monitoring, and CI/CD across cloud environments.Collaborating with data engineers and architects to build scalable data & model workflows.Communicating insights and technical decisions to both business and technical stakeholders.Preparing documentation and maintaining clear, structured model repositories and experiment tracking.Staying current with advancements in AI/ML, LLMs, and emerging applied AI techniquesWhat You'll Bring AlongBachelor’s degree in Computer Science, Information Technology, Business Administration, or a related field is preferedMinimum 3-5 years of hands-on experience building ML / DL models (classification, extraction, NLP or embeddings).Strong proficiency in Python and frameworks such as PyTorch, TensorFlow, Scikit-learn.Experience with LLM-based solutions (e.g., fine-tuning, prompt engineering, embedding-based retrieval, RAG).Familiarity with vector databases (Pinecone, ChromaDB, FAISS) and LLM orchestration frameworks (LangChain / LlamaIndex).Experience with at least one MLOps / lifecycle platform (Azure ML, MLflow, SageMaker, Kubeflow).Understanding of cloud environments (Azure, AWS, or GCP) and containerization using Docker.Ability to design and document model workflows, track experiments, and monitor deployed models.Comfortable working with cross-functional teams and translating requirements into model design.Clear communication skills and ability to explain trade-offs to technical and non-technical audiences.