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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
Responsibilities
- 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 Mastercard's 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
Additional Responsibilities
- Abide by Mastercard's 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
Nice To Have
- 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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