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Senior AI Data Engineer
Mastercard (company site, opens in a new tab)PuneOn-siteDirect career pageEst. 28–45 LPA
Posted 7h ago
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Job description
Structured overview of role & requirements
About This Role
- Lead design, development, and optimization of scalable, cloud-native data platforms and pipelines for AI, machine learning, and Generative AI solutions.
- Build production-grade data infrastructure supporting AI lifecycle stages including data ingestion, feature engineering, model training, inference, and continuous improvement.
- Ensure data quality, governance, security, operational excellence, and collaborate cross-functionally to enable enterprise-scale AI initiatives.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.
- Significant experience designing and implementing enterprise-scale data engineering solutions supporting AI and machine learning workloads.
- Proficiency in Python, SQL, Spark, distributed data processing, cloud platforms (AWS, Azure, or GCP), and streaming technologies (Kafka, Kinesis, or Azure Event Hubs).
- Experience with workflow orchestration tools (e.g., Apache Airflow, Azure Data Factory) and supporting data governance, monitoring, and security for production data pipelines.
Ideal Candidate Profile
- Experienced in building and optimizing data architectures for AI including LLMs, embeddings, vector databases, RAG, and AI knowledge repositories.
- Skilled in implementing automation, CI/CD, Infrastructure as Code, and MLOps/AgenticOps to support scalable and reliable AI workflows.
- Capable of delivering reusable enterprise data products and collaborating with AI Engineers, Data Scientists, and Platform Engineers in a complex, regulated enterprise environment.