Experience:

• Considerable work experience with proven track record of leading and managing complex projects/products
• Experience delivering to aggressive market needs

Qualification:

• Master’s degree in computer science or engineering
• Bachelor’s degree in computer science or engineering

Responsibilities:

• Lead ML engineering team to execute AI/ML strategy for Operational Intelligence Program
• Implement multi-agent intelligence frameworks for reasoning, coordination, and adaptive decision-making
• Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data
• Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs
• Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction
• Implement Python-based backend services for inference orchestration, async job handling, and distributed workflows
• Establish end-to-end LLMOps and MLOps pipelines integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining
• Apply traditional AI/ML and statistical modeling techniques alongside deep learning models
• Engineer state and memory management subsystems for temporal reasoning across multiple modalities
• Implement Responsible AI practices including bias detection, explainability dashboards, data ethics checks, and performance governance
• Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration

Additional responsibilities:

• Foster a culture of continuous learning and technical excellence within the team
• Focus on engineering best practices, next generation innovation, and stakeholder management
• Abide by Mastercard’s security policies and practices
• Ensure confidentiality and integrity of accessed information
• Report suspected information security violations or breaches
• Complete all mandatory security trainings as per Mastercard guidelines

Nice to have:

• Experience with data visualization tools such as Tableau and Power BI
• Understanding of cloud computing services related to data processing and storage (AWS, Azure, GCP)

More skills:

Machine Learning, AI, Agentic solutions, Databricks, MLflow, Feature stores, Transformer-based architectures, LangGraph, CrewAI, AutoGen, CLIP, LLaVA, T5, Whisper, Graph-RAG, Neo4j, AWS Neptune, Async job handling, Distributed data workflows, Statistical modeling, Regression, Clustering, Forecasting, Ensemble methods, Deep learning, Data visualization tools, Tableau, Power BI, Cloud computing services, AWS, Azure, GCP