Senior AI Engineer
Mastercard22 hours ago
Pune, IndiaSenior
Responsibilities
- Architect and build secure, well-defined tool-calling layers that connect agents to internal services, data platforms, and external APIs.
- Engineer reliable, scalable tool execution with parallel and chained calls, retries, timeouts, idempotency, error recovery, and failure handling.
- Develop high-performance Python backend services for LLM orchestration, asynchronous processing, streaming, caching, rate limiting, and distributed workflows.
- Build agent state, memory, context management, multi-agent systems, retrieval systems, observability, and agent evaluation capabilities.
- Establish LLMOps and MLOps pipelines, including model evaluation, lineage tracking, deployment workflows, and monitoring.
- Design multimodal generative AI pipelines and guide fine-tuning, prompt engineering, alignment, responsible AI, and safety practices.
- Evaluate and productionize advances in agentic AI, multimodal models, and foundation models against enterprise performance and safety requirements.
- Lead, mentor, and grow engineers while setting Gen AI 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, including safely and reliably connecting services and LLMs.
- Excellent Python engineering skills, including asynchronous programming, API development, microservices, and SQL proficiency.
- Hands-on experience shipping at least one production LLM-powered application using tool or function calling.
- Working knowledge of prompt design, embeddings, semantic search, agent memory, and generative AI engineering patterns, 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 with Model Context Protocol, tool servers and connectors, agent performance evaluation, fine-tuning, multimodal models, knowledge graphs, or 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).