Python Engineering AI Lead-Assistant Vice president

• Design, develop, and maintain scalable, enterprise-grade AI agents , supporting ELT/ETL processes to handle large data volumes using the Python, FAST API, Microservices , PySpark, Kafka and Databricks ecosystem.

• Build and Deploy GEN AI Agents using Googles ADK and Google Flash 2.5+ LLMs to support application automation supports and its deep insights, workflow support with HIL - Human in loop architecture.

• Build and maintain data federation layers for lambda and Data Mesh architectures using tools like Starburst, with a strategy for adopting AI-based use cases (e.g., machine learning, deep learning, NLP) to drive efficiency.

• Develop, deploy, and automate microservice integrations to support data-intensive applications, ensuring scalability, resilience, and maintainability using cloud native infrastructure and openshift or Kubernates architecture including CI/CD pipelines.

• Integrate and leverage agentic AI tools (e.g., Devin.AI, Github Copilot) and platforms (e.g., MCP) through advanced prompt engineering to enhance development and operational efficiency.

• Ensure data quality, integrity, and security throughout the entire data lifecycle.

• Contribute to the continuous improvement of data engineering processes, standards, and best practices within the team.

• Appropriately assess risk when business decisions are made, demonstrating consideration for the firm's reputation and safeguarding Citi, its clients, and assets by driving compliance with applicable laws, rules, and regulations. Adhere to Policy, apply sound ethical judgment, and escalate, manage, and report control issues with transparency.

Qualifications

Required:

• 8+ years of overall experience in large-scale application development with recent mandatory platform for the secure and scalable deployment of AI agents into application contexts

• Minimum of 5+ years of proven experience in a Python and pyspark Engineering lead role focused on building enterprise-grade, high-volume ELT/ETL processes using thePySpark and Databricks ecosystem.

• Hands-on experience with agentic AI development using YAML, JSON, FAST API or Spring boot, Google ADK, LLM itegrations, includingDevin.AI or Github Copilot, and integrating models via platforms likeMCPusing advanced prompt engineering.

• Proven experience developing andautomating microservice integrationsto support data-intensive applications.

• Proficiency in at least one programming language commonly used for data analytics, engineering, such asPython or Scala.

• Strong SQL skills and experience with various relational databases.

• Deep understanding of data modeling, data warehousing concepts, Data Mesh architecture, and data federation.

  • Excellent communication, collaboration, and problem-solving skills.