Lead AI Engineer at Mastercard

Department: AI & Data

Location: Gurgaon, Haryana
Level: lead/mgmt
Posted: Sep 24, 2026

Description

Mastercard is hiring a Lead AI Engineer to build and operate production MLOps capabilities, model deployment pipelines, Databricks workspaces, infrastructure automation, feature and model monitoring, and CI/CD workflows. The role partners with data science, platform, and product teams and mentors junior engineers. It requires a master’s degree with 3+ years of relevant experience or a bachelor’s degree with 5+ years, along with hands-on MLOps, Databricks, Spark, Python, PySpark, SQL, cloud, and infrastructure-as-code experience.

Required Skills

MLOps
Databricks
Spark
Python
PySpark
SQL
CI/CD
Terraform
Kubernetes

Additional Information

Minimum Education: Master’s degree with 3+ years of relevant experience, or Bachelor’s degree with 5+ years, in a relevant field; equivalent practical experience considered.
Required Previous Experiences: Hands-on MLOps experience across model monitoring, feature catalogs, experiment tracking, model registry, and lifecycle CI/CD pipelines.
Hands-on experience administering Databricks workspaces, including cluster and compute policies, job orchestration, runtime and library upgrades, permissions, and secrets.
Experience deploying and maintaining infrastructure through infrastructure as code and automated pipelines, including environment provisioning, configuration management, and controlled release and rollback.
Strong experience with Spark and distributed data processing.
Hands-on experience with CI/CD and build tooling such as Git, Jenkins, Maven, and Artifactory.
Experience building and optimizing feature engineering and large-scale data processing workflows.
Experience with model deployment, evaluation, observability, optimization, and operational support.
Experience with cloud operations across public and private cloud environments.
Preferred Qualifications: Experience with MLOps tools such as MLflow, Comet, or Weights and Biases.
Experience automating Databricks administration and deployment with the Databricks CLI, REST APIs, Asset Bundles, or the Databricks Terraform provider.
Experience with infrastructure as code and configuration tooling such as Terraform or Ansible, and with Docker and Kubernetes.
Experience contributing to engineering standards, governance frameworks, or observability practices for production AI systems.
Experience with Generative AI, LLMs, RAG, or agentic AI applications.
Familiarity with AI-assisted development tools such as GitHub Copilot or Claude Code.
Experience with data governance tooling, including data catalogs, lineage, role-based access control, and sensitive data handling.