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Tier-1 brand and Pune metro increase candidate density, but role specialization limits broader applicant pool.
Requires deep ML/AI, LLM, MLOps and cloud expertise, limiting cross-industry transferability.
Extensive mandatory ML/AI production experience, LLM, MLOps, cloud, and containerization make filters highly stringent.
Design, develop, deploy, and operate scalable, secure enterprise AI and Agentic AI applications and platforms for Mastercard's products and internal functions.
Lead productionization of AI solutions including end-to-end AI pipelines, infrastructure, MLOps/AgenticOps practices, and ensure adherence to Mastercard’s reliability, security, governance, and compliance standards.
Partner with cross-functional teams to build reusable AI capabilities and deliver AI innovations aligned with Mastercard’s strategic business goals.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or related field.
Extensive experience building and deploying production AI/ML systems in enterprise environments.
Proficient in Python, Java, or similar; experience with cloud-native development on AWS, Azure, or GCP.
Experience with containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, MLOps frameworks, LLMs, vector databases, and AI governance.
Experienced in transforming AI prototypes and research into scalable, maintainable production-grade solutions with strong operational ownership.
Strong engineering background with expertise in distributed systems, microservices, event-driven architectures, and cloud platforms.
Capable of influencing technical decisions, establishing best practices for MLOps/AgenticOps, and collaborating effectively with multidisciplinary teams to align AI initiatives with business objectives.