Lead AI Engineer

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• Lead AI Engineer
• Mastercard
• Pune
• 5-8 years
• 1 day ago
• $39.8K–60.2K/yr
• Full-time
• Onsite

Skills Required

• LLM
• Gen AI
• Vector Database
• Agentic AI
• retrieval systems
• model evaluation
• model governance
• model training
• model deployment
• MLOps
• drift detection
• telemetry
• operational analytics
• AI gateways
• Python

Description

Mastercard is seeking a Lead AI Engineer to design, implement, and operate enterprise AI platforms that support AI and machine learning workloads across public and private cloud environments.

Company: Mastercard

Role: Lead AI Engineer

Experience

• Experience designing, building, and operating cloud-native systems in enterprise environments
• Strong experience working within both public and private cloud environments
• Experience deploying and managing containerized workloads using Kubernetes or OpenShift
• Strong software engineering and automation experience using Python
• Experience implementing CI/CD pipelines and modern DevOps practices
• Experience supporting production AI, machine learning, data platforms, or large-scale distributed systems
• Strong understanding of MLOps principles and machine learning lifecycle management
• Experience implementing monitoring, observability, telemetry, logging, and operational analytics solutions
• Experience working in highly collaborative, cross-functional engineering environments

• Design, build, and operate enterprise AI platforms supporting machine learning, generative AI, and advanced analytics workloads
• Engineer scalable solutions across public and private cloud environments ensuring security, reliability, availability, and performance
• Build and automate platform capabilities for onboarding, deployment, operations, and lifecycle management of AI solutions
• Develop and maintain infrastructure, tooling, and services supporting model training, evaluation, deployment, monitoring, and governance
• Implement and enhance MLOps capabilities for repeatable, scalable, and secure AI development workflows
• Design and maintain observability solutions including telemetry, performance monitoring, logging, alerting, operational analytics, and drift detection
• Support production AI platforms and services to improve reliability, scalability, efficiency, and customer experience
• Partner with internal engineering teams to understand requirements, enable platform adoption, and accelerate AI product delivery
• Collaborate with infrastructure, security, architecture, and governance teams to ensure alignment with enterprise standards and regulatory requirements
• Evaluate emerging AI technologies and platform capabilities to shape Mastercards AI ecosystem
• Drive automation and engineering best practices through Infrastructure as Code, CI/CD, testing, and operational excellence
• Participate in troubleshooting, root cause analysis, operational support, and incident response to maintain platform availability
• Contribute to technical design discussions, architecture reviews, and long-term platform strategy
• Mentor peers and share knowledge across engineering teams fostering continuous improvement

• Abide by Mastercards security policies and practices
• Ensure confidentiality and integrity of accessed information
• Report any suspected information security violation or breach
• Complete all mandatory security trainings per Mastercard guidelines

• Experience supporting generative AI platforms and large language model workloads
• Experience implementing model evaluation, finetuning, guardrails, and governance controls
• Experience with model observability, drift detection, telemetry monitoring, and operational analytics
• Experience with AI serving infrastructure and inference platforms
• Experience supporting GPU-based workloads and accelerated computing environments
• Experience with Docker, Helm, GitOps, and Infrastructure as Code practices
• Experience with enterprise-scale platform engineering, developer enablement, and self-service platform capabilities
• Familiarity with vector databases, retrieval systems, AI gateways, agentic systems, or emerging AI platform technologies
• Experience working within regulated environments requiring strong security, governance, and compliance controls
• Proficiency in deep learning, neural networks, iterative model development, hyperparameter tuning, reinforcement learning, and feedback loops
  More Skills
• Cloud-native systems, Public and private cloud, Kubernetes, OpenShift, CI/CD pipelines, DevOps practices, Monitoring, Observability, Logging, Machine learning lifecycle management
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