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 or 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, hallucination mitigation, and explainability
• Apply traditional ML and statistical modeling in hybrid architectures alongside LLMs
• Continuously research, evaluate, 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 all mandatory security trainings per Mastercard guidelines

More skills:

Large Language Models (LLM), Agentic AI systems, Multimodal AI, LangGraph, CrewAI, AutoGen, Transformer architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini), Retrieval-Augmented Generation (RAG), Graph-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, Prompt evaluation frameworks, Model lineage tracking, Python, Async programming, FastAPI, SQL, Cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, GCP Vertex AI), Weights & Biases, PyTorch, TensorFlow, Statistical modeling, Regression, Clustering, Forecasting, Ensemble methods

Other:

• Full time job
• Category: AI & Data
• Job Id: R-277214