Lead AI Engineer
Mastercard24 hours ago
Gurgaon, IndiaStaff+
Responsibilities
- Design, develop, and maintain MLOps capabilities and production AI and machine learning systems.
- Build scalable model training pipelines, deployment frameworks, feature engineering workflows, and data ingestion and preprocessing systems.
- Administer Databricks workspaces, including provisioning, compute policies, job orchestration, upgrades, access management, secrets, monitoring, and cost optimization.
- Deploy and maintain AI infrastructure using infrastructure as code and automated release pipelines.
- Automate model training, testing, deployment, updates, monitoring, and lifecycle workflows.
- Build domain-specific feature and model monitoring systems that track performance and drift.
- Establish onboarding, packaging, validation, governance, and operating standards for platform users.
- Partner with data science, platform, and product teams to translate requirements into technical solutions.
- Mentor engineers through on-the-job guidance, code reviews, and design reviews.
Requirements
- Master’s degree with at least 3 years of relevant experience or Bachelor’s degree with at least 5 years of relevant experience in computer science, artificial intelligence, machine learning, data science, engineering, or a related field; equivalent practical experience is considered.
- Hands-on MLOps experience with model monitoring, feature catalogs, experiment tracking, model registries, and lifecycle pipelines.
- Hands-on Databricks administration experience, including cluster and compute policies, job orchestration, upgrades, permissions, and secrets.
- Experience deploying and maintaining infrastructure through infrastructure as code, configuration management, automated pipelines, and controlled release and rollback.
- Strong experience with Spark and distributed data processing.
- Proficiency in Python, PySpark, and SQL.
- Hands-on experience with Git, Jenkins, Maven, and Artifactory.
- Experience building and optimizing feature engineering and large-scale data processing workflows.
- Strong understanding of machine learning, deep learning, model lifecycle management, and production AI systems.
- Experience with model deployment, evaluation, observability, optimization, and operational support.
- Experience with cloud operations across public and private cloud environments.
- Ability to communicate technical concepts clearly, work independently, and mentor engineers.
- Preferred experience includes MLflow, Comet, Weights and Biases, Databricks CLI, Databricks REST APIs, Asset Bundles, the Databricks Terraform provider, Terraform, Ansible, Docker, Kubernetes, production AI governance and observability, generative AI, LLMs, RAG, agentic AI applications, GitHub Copilot, Claude Code, and data governance tooling.
Tech Stack
Categories
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).