Overview Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive. Key Responsibilities Design and develop Generative AI solutions, including LLM-based applications Implement techniques such as prompt engineering, Retrieval-Augmented Generation (RAG), and fine‑tuning Develop and evaluate machine learning models across supervised, unsupervised, and NLP use cases Optimise model performance, reliability and cost efficiency for enterprise environments Data & Engineering Collaboration Work with structured and unstructured data sources (databases, APIs, documents, transcripts, logs) Collaborate with data engineers to support ingestion, preparation and transformation pipelines Ensure AI solutions are scalable, maintainable and aligned with software engineering best practices Deployment & Production Package and deploy AI and GenAI solutions into production (APIs, services, batch workflows) Support cloud‑based deployments, primarily on Microsoft Azure, including Azure OpenAI and Azure AI services Apply MLOps and LLMOps practices such as versioning, monitoring, evaluation and continuous improvement Client & Stakeholder Engagement Translate business problems into effective AI and GenAI use cases Contribute to proofs of concept, pilots and enterprise‑scale implementations Support solution architects and senior AI leaders in client engagements and technical delivery Qualification Requirements Postgraduate level qualification (Honours or Masters) in Computer Science, Data Science, Engineering, Mathematics, Statistics, Economics or a related field Alternatively, equivalent practical experience delivering AI or machine learning solutions in enterprise environments Required Skills Core Technical Skills Strong proficiency in Python for AI and machine learning development Solid understanding of machine learning fundamentals and evaluation techniques Hands‑on experience with ML frameworks such as scikit‑learn, PyTorch, or TensorFlow Experience working with Generative AI and LLMs in practical use cases Strong data handling skills, including SQL and data querying Experience deploying models or AI services into production environments Experience with vector databases and embeddings (e.g., for RAG architectures) Cloud & Platforms Experience with cloud platforms (Azure preferred; AWS or GCP acceptable) Familiarity with Azure OpenAI, Azure AI services, or similar GenAI platforms Experience building or consuming REST APIs and using containerisation tools such as Docker Exposure to MLOps / LLMOps tools and practices (e.g., MLflow, monitoring, CI/CD) Experience & Profile 5–10 years of experience in AI, machine learning, or applied data science roles Demonstrated experience delivering real‑world AI or GenAI solutions beyond experimentation Comfortable working in a hybrid, client‑facing consulting environment Strong communication skills and a pragmatic, delivery‑oriented mindset Equal Opportunity Employer NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity. We are committed to providing an environment free of unfair discrimination and harassment. We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status or any other protected category. #J-18808-Ljbffr