Experience:

• Significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments
• Expert-level hands-on experience designing, building, and deploying large language model applications, agentic systems, and RAG pipelines

Qualification:

• Master's degree in Computer Science, AI/ML, or Engineering
• Bachelor's degree in Computer Science, AI/ML, or Engineering

Responsibilities:

• Architect and lead development of multi-agent AI systems enabling autonomous reasoning and adaptive decision-making
• Design and operationalize multimodal generative AI pipelines unifying text, image, tabular, and graph data
• Build production-grade RAG and Graph-RAG systems integrating vector databases and knowledge graphs
• Lead LLM fine-tuning, prompt engineering, and model alignment strategies
• Establish robust LLMOps and MLOps pipelines ensuring reliable AI delivery
• Develop high-performance Python backend services for LLM inference orchestration and distributed workflows
• Engineer state, memory, and context management subsystems for agent reasoning and session continuity
• Implement Responsible AI and AI governance practices including bias detection and explainability
• Apply traditional ML and statistical modeling in hybrid architectures alongside LLMs
• Continuously research and productionize advancements in generative modeling and frontier foundation models

Additional responsibilities:

• Translate complex Gen AI concepts into business value
• Lead cross-functional teams toward delivery
• Abide by Mastercard’s security policies and practices
• Ensure confidentiality and integrity of accessed information
• Report suspected information security violations or breaches
• Complete mandatory security trainings per Mastercard guidelines

More skills:

Large Language Models (LLM), LangGraph, CrewAI, AutoGen, Transformer architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini), Retrieval-Augmented Generation (RAG), Vector databases (Pinecone, pgvector, OpenSearch), Knowledge graphs (Neo4j, AWS Neptune), LLM fine-tuning, RLHF, PEFT, LoRA, Instruction tuning, MLOps, Databricks (AWS), MLflow, Feature stores, Python, Async programming, FastAPI, SQL, Cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, GCP Vertex AI), Weights & Biases, PyTorch, TensorFlow, Statistical modeling, Machine learning fundamentals

Other:

• Work within Mastercard’s Business & Market Insights group
• Focus on enabling business growth, elevating customer experience, and ensuring secure, scalable AI
• Drive innovation across agentic orchestration, retrieval-augmented generation, LLMOps, and responsible AI
• Foster a culture of continuous learning and engineering excellence