Python Engineering AI Lead-Assistant Vice president

Citi · Chennai · 8+ yrs experience · Posted 2026-07-18

Tech stack: AWS, Angular, Azure, Docker, GCP, Kafka, Kubernetes, Python, React, SQL, Scala, Spring Boot

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About the role

Job Summary We are seeking a highly motivated and experienced Principal Engineer to join our Retail and Wealth Risk Engineering team under the Enterprise Risk Technology platform. This is an intermediate-level position responsible for designing, building, and maintaining robust, scalable data pipelines and solutions that leverage cutting-edge
Responsibilities: - 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: - 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 the Py
- Spark and Databricks ecosystem
- Hands-on experience with agentic AI development using YAML, JSON, FAST API or Spring boot, Google ADK, LLM itegrations, including
- Devin.AI or Github Copilot and integrating models via platforms like using advanced prompt engineering.
- Proven experience developing and automating microservice integrations to support data-intensive applications.
- Proficiency in at least one programming language commonly used for data analytics, engineering, such as Python 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.
- with cloud-based Big Data platforms (e.g.
- Cloudera, Databricks, AWS, Azure, GCP with frontend technologies such as Angular or React JS for building data-driven application interfaces.
- Practical experience applying AI/ML techniques to solve real-world business problems.
- Familiarity with containerization technologies (e.g., Docker, Kubernetes).
- in data engineering within the banking retail products domain (e.g., Cards, Mortgage, Deposits, Wealth Management).
- Relevant industry certifications (e.g., AWS Certified Big Data Specialty, Azure Data Engineer Associate).
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Master’s degree is a plus.

Qualifications

- 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 the Py
- Spark and Databricks ecosystem
- Hands-on experience with agentic AI development using YAML, JSON, FAST API or Spring boot, Google ADK, LLM itegrations, including
- Devin.AI or Github Copilot and integrating models via platforms like using advanced prompt engineering.
- Proven experience developing and automating microservice integrations to support data-intensive applications.
- Proficiency in at least one programming language commonly used for data analytics, engineering, such as Python 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.
- with cloud-based Big Data platforms (e.g.
- Cloudera, Databricks, AWS, Azure, GCP with frontend technologies such as Angular or React JS for building data-driven application interfaces.
- Practical experience applying AI/ML techniques to solve real-world business problems.
- Familiarity with containerization technologies (e.g., Docker, Kubernetes).
- in data engineering within the banking retail products domain (e.g., Cards, Mortgage, Deposits, Wealth Management).
- Relevant industry certifications (e.g., AWS Certified Big Data Specialty, Azure Data Engineer Associate).
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Master’s degree is a plus.

Responsibilities

- 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.

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