Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
About the role
Title and Summary AI Tech Lead Job Description Summary We are seeking an experienced AI Tech Lead to lead the design and development of scalable backend systems and production-grade AI applications.
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
AI Tech Lead
Job Description Summary
We are seeking an experienced AI Tech Lead to lead the design and development of scalable backend systems and production-grade AI applications.
This is a hands-on technical leadership role combining deep Python/backend engineering expertise with proven experience building and deploying GenAI applications and AI agents used by end users in production.
You will shape the architecture of our AI-powered products, build scalable backend services and agentic AI solutions, and lead engineers in delivering reliable, high-performance systems at scale.
Key Responsibilities
Backend & Technical Leadership
Lead the architecture and development of scalable Python backend systems, services, and APIs.
Design distributed systems and microservices with a focus on performance, scalability, reliability, and security.
Remain hands-on with architecture, coding, code reviews, and complex technical problem-solving.
Define engineering best practices across backend and AI development.
Show the full description
AI & Agentic Engineering
Design and build production-grade GenAI applications and AI agents used by end users.
Build agentic workflows including LLM orchestration, tool calling, context management, memory, and service integrations.
Integrate LLMs and other AI models into scalable production applications.
Build solutions using technologies such as Amazon Bedrock, OpenAI, LangChain, and LangGraph.
Implement LLM evaluation, tracing, monitoring, and observability.
Optimize AI solutions for quality, latency, reliability, scalability, and cost.
Take AI capabilities from experimentation and POC through to production and end-user adoption.
Team Leadership & Delivery
Lead, mentor, and provide technical guidance to backend and/or full-stack engineers.
Partner with Product, AI/Data, DevOps, and Architecture teams to define and deliver technical solutions.
Drive complex projects from design through deployment and production monitoring.
Foster a strong engineering culture focused on ownership, innovation, and quality.
Required Qualifications
8+ years of software engineering experience with strong backend development expertise.
What they ask for vs. your profile
Must have
LLMs
Python
Leadership
Generative AI
AWS
Docker
Kafka
Kubernetes
Microservices
Node.js
Strong hands-on Python experience, particularly building production backend services and applications.
2+ years in a technical leadership or Tech Lead role.
Proven experience designing large-scale distributed systems and microservices.
Hands-on experience building and deploying GenAI applications and AI agents in production.
Experience taking LLM/agentic solutions beyond POC into applications used by end users.
Experience with LLMs, agent orchestration, tool calling, context management, and AI/backend integrations.
Experience with Amazon Bedrock, OpenAI, LangChain, LangGraph, or similar technologies.
Understanding of production AI considerations including evaluation, observability, latency, reliability, scalability, and cost.
Experience with cloud platforms, preferably AWS, as well as Docker and Kubernetes.
Strong system design and problem-solving skills.
Preferred Qualifications
Experience with multi-step or multi-agent AI systems.
Experience with LLM evaluation and observability tools such as Langfuse.
Experience with RAG, retrieval systems, embeddings, or vector search.
Experience with real-time data technologies such as Kafka, Spark, or Flink.
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
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, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
