01Responsibilities
Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents.
Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence.
Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning.
Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems.
Implement advanced Python-based backend services for inference orchestration, async job handling, and distributed data workflows supporting high-throughput AI operations.
Establish end-to-end LLMOps and MLOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining frameworks.
Apply traditional AI/ML and statistical modeling techniques (regression, clustering, forecasting, ensemble methods) alongside deep learning models for hybrid interpretability and explainability.
Engineer state and memory management subsystems that preserve context, track embeddings, and enable agents to reason temporally across multiple modalities and interactions.
Implement Responsible AI practicesbias detection, explainability dashboards, data ethics checks, and performance governance ensuring fairness and transparency of deployed models.
Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration to drive enterprise-scale intelligence and automation.
All About You:
Masters/bachelors degree in computer science or engineering, and a considerable work experience with a proven track-record of successfully leading and managing complex projects/products and delivering to aggressive market needs.
Expert-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions.
Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems.
Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks. Experience with AI and machine learning platforms such as TensorFlow, PyTorch, or similar.
Strong programming skills in languages such as Python/SQL is a must. Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Abide by Mastercards security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security .
