Senior AI Engineer
About The Position
Mastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth and innovation goals by delivering unparalleled data-driven intelligence and cutting-edge AI solutions. By harnessing proprietary data, frontier generative AI, and global expertise, B&MI helps businesses make smarter, faster, and more impactful decisions. We transform complex, multi-modal data into agentic systems and generative applications that drive measurable business outcomes and sustained competitive advantage. We are looking for a Senior AI Engineer, Generative AI & ML Engineering for the Operational Intelligence Program within B&MI. This role will expect Gen AI engineer to architect and deliver next-generation LLM, agentic, and multimodal AI systems that enable business growth, elevate customer experience, and ensure secure, scalable, production-grade AI. As a technical leader, you will set the engineering standard for Gen AI development — driving innovation across agentic orchestration, retrieval-augmented generation, LLMOps, and responsible AI — while fostering a culture of continuous learning and engineering excellence.
Requirements
- 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.
- Deep proficiency with LLM ecosystems: OpenAI, Anthropic, Gemini, Hugging Face, LangChain/LangGraph, and open-source foundation models (LLaMA, Mistral, Falcon, etc.).
- Strong command of Gen AI engineering patterns: prompt engineering, chain-of-thought reasoning, tool/function calling, vector embeddings, semantic search, and agent memory architectures.
- Solid applied knowledge of ML fundamentals — predictive modeling, deep learning (PyTorch, TensorFlow), and statistical techniques — used in tandem with Gen AI for hybrid, interpretable systems.
- Excellent Python engineering skills including async programming, API development (FastAPI), and building inference-ready microservices; SQL proficiency required.
- Hands-on experience with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and familiarity with MLOps/LLMOps tooling (MLflow, Weights & Biases, etc.).
- Strong analytical, communication, and stakeholder management skills — with the ability to translate complex Gen AI concepts into business value and lead cross-functional teams toward delivery.
Responsibilities
- Architect and lead the development of multi-agent AI systems using frameworks such as LangGraph, CrewAI, and AutoGen — enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.
- Design and operationalize multimodal generative AI pipelines that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.
- Build production-grade RAG and Graph-RAG systems integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.
- Lead LLM fine-tuning, prompt engineering, and model alignment strategies — including RLHF, PEFT, LoRA, and instruction tuning — to adapt foundation models for specialized enterprise use cases.
- Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.
- Develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.
- Engineer state, memory, and context management subsystems that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.
- Implement Responsible AI and AI governance practices — including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards — ensuring transparency and fairness of deployed models.
- Apply traditional ML and statistical modeling (regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.
- Continuously research, evaluate, and productionize advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models — benchmarking against enterprise-scale performance and safety requirements.
Benefits
- insurance (including medical, prescription drug, dental, vision, disability, life insurance)
- flexible spending account and health savings account
- paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave)
- 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire
- 10 annual paid U.S. observed holidays
- 401k with a best-in-class company match
- deferred compensation for eligible roles
- fitness reimbursement or on-site fitness facilities
- eligibility for tuition reimbursement
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What This Job Offers
Job Type
Full-time
Career Level
Senior
