Experience:

• Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering, with 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 (LLM) applications, agentic systems, and RAG pipelines — from prototype to production.

Qualification:

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

Role Focus:

• Architect and lead the development of multi-agent AI systems
• Design and operationalize multimodal generative AI pipelines
• Build production-grade RAG and Graph-RAG systems
• Lead LLM fine-tuning, prompt engineering, and model alignment strategies
• Establish robust LLMOps and MLOps pipelines
• Develop high-performance Python backend services
• Engineer state, memory, and context management subsystems
• Implement Responsible AI and AI governance practices

More skills:

transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini), vector databases (Pinecone, pgvector, OpenSearch), knowledge graphs (Neo4j, AWS Neptune), LLM fine-tuning, prompt engineering, model alignment strategies, Databricks (AWS), MLflow, async programming, API development (FastAPI), SQL, cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI), MLOps/LLMOps tooling (MLflow, Weights & Biases, etc.)

Other:

• Strong analytical, communication, and stakeholder management skills
• Ability to translate complex Gen AI concepts into business value and lead cross-functional teams toward delivery
• Corporate Security Responsibility: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach; Complete all periodic mandatory security trainings