Senior AI Engineer
Mastercard21 hours ago
Pune, IndiaSenior
Responsibilities
- Architect and build secure, schema-driven tool-calling layers integrating internal services, data platforms, and external APIs.
- Engineer reliable, scalable tool execution with parallel and chained calls, retries, timeouts, idempotency, error recovery, and graceful handling of failures.
- Develop Python backend services for LLM orchestration, including asynchronous processing, streaming, caching, rate limiting, and distributed workflows.
- Build agent state, memory, context management, multi-agent systems, skills-based agents, and production observability.
- Develop RAG and Graph-RAG retrieval systems using vector databases and knowledge graphs.
- Establish LLMOps and MLOps pipelines covering evaluation, lineage tracking, deployment, and monitoring.
- Design multimodal generative AI pipelines and guide fine-tuning, prompt engineering, and alignment strategies.
- Implement responsible AI practices including hallucination mitigation, bias detection, safety guardrails, explainability, and data-ethics compliance.
- Evaluate and productionize agentic AI, multimodal-model, and foundation-model advances against enterprise requirements.
- Lead, mentor, and grow engineers while setting GenAI engineering standards across the program.
Requirements
- Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering, or equivalent practical experience.
- Strong track record building and operating production backend systems at scale, including distributed services, APIs, asynchronous processing, data stores, and observability.
- Strong API design and integration experience with an ability to build safe and reliable callable services.
- Excellent Python skills, including asynchronous programming, API development with FastAPI or similar, microservices, and SQL proficiency.
- Hands-on experience shipping at least one production LLM-powered application using tool or function calling.
- Working understanding of prompt design, embeddings, semantic search, and agent memory, or demonstrated ability to learn new technical domains quickly.
- Experience with at least one cloud platform, with AWS preferred and Azure or GCP accepted.
- Experience leading or mentoring engineers, communicating effectively, managing stakeholders, and translating technical work into business value.
- Preferred experience includes Model Context Protocol, tool servers and connectors, agent-performance evaluation, fine-tuning, multimodal models, knowledge graphs, and MLOps tooling such as MLflow, Weights & Biases, SageMaker, or Bedrock.
Tech Stack
Categories
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).