Job Overview

Citi is hiring a Python and AI Data Engineering Lead - Senior Vice President in Pune, Maharashtra, with a hybrid work model. This is a senior individual contributor opportunity for an experienced data and AI engineering professional with 12+ years of experience in the data domain. The role focuses on designing and delivering scalable enterprise solutions for large-volume data environments while combining modern data engineering, AI engineering, application development, and intelligent automation.

The position requires deep hands-on engineering capability rather than a traditional people-management focus. You will work across data pipelines, ETL processing, databases, APIs, AI frameworks, large language models, agentic AI systems, and cloud-native engineering practices. The role also expects strong attention to performance, reliability, security, testing, and maintainability.

Key Responsibilities

• Design, build, maintain, and optimize ETL processes that move data from multiple sources into enterprise data warehouse environments.
• Develop complex SQL queries and PL/SQL scripts for data transformation, validation, manipulation, and processing.
• Create and maintain scalable data pipelines using Python and related data libraries.
• Work with large-volume datasets and improve pipeline performance, reliability, and scalability.
• Collaborate with data analysts and other stakeholders to understand data requirements and translate them into practical technical solutions.
• Implement data quality checks, validation mechanisms, monitoring, and controls to improve data accuracy and consistency.
• Investigate and resolve data engineering and pipeline issues while maintaining operational stability.
• Prepare and maintain technical documentation covering ETL processes, data pipelines, databases, APIs, and AI engineering solutions.
• Apply AI engineering practices such as chunking, embeddings, and prompt engineering to enterprise use cases.
• Design and implement scalable AI services and robust APIs.
• Build or integrate agentic AI solutions using modern frameworks and model technologies.
• Work with large language models and apply them to practical enterprise applications.
• Design solutions using API-first, microservices, and event-driven architecture patterns.
• Apply software engineering practices including Git, CI/CD, testing, code reviews, and Agile delivery.
• Support secure, resilient, and operationally stable AI and data engineering initiatives.
• Use Docker and OpenShift for application packaging, deployment, and platform integration.

Required Skills

Strong Python engineering experience is central to this position. Candidates should be comfortable developing enterprise applications and data services using Python frameworks such as FastAPI, Flask, and PySpark. Java expertise with technologies such as Spring Boot, Spring Cloud, and Spring Security is also relevant.

Strong database knowledge is required, including Oracle and SQL, with experience in enterprise data processing and large-volume workloads. Experience with PostgreSQL and MongoDB is also valuable.

Candidates should understand full-stack technologies such as Angular, React, Node.js, and TypeScript, particularly when developing integrated AI or data applications.

The role also requires a strong foundation in AI engineering. Relevant knowledge includes knowledge representation, automated planning, decision-making under uncertainty, multi-agent systems, embeddings, chunking, and prompt engineering.

Required Technologies

• Python
• FastAPI
• Flask
• PySpark
• Oracle
• SQL
• PL/SQL
• PostgreSQL
• MongoDB
• Java
• Spring Boot
• Spring Cloud
• Spring Security
• Angular
• React
• Node.js
• TypeScript
• Google ADK
• LangGraph
• LangChain
• AutoGen
• CrewAI
• N8N
• TensorFlow
• PyTorch
• Scikit-Learn
• NumPy
• Pandas
• Git
• CI/CD
• Docker
• OpenShift
• Microservices
• REST APIs
• Event-Driven Architecture
• API-First Design
• Large Language Models
• Agentic AI
• Model Context Protocol (MCP)

AI and Machine Learning Experience

Hands-on experience with machine learning frameworks such as TensorFlow and PyTorch is expected, together with practical knowledge of libraries including Scikit-Learn, NumPy, and Pandas.

The role involves working with large language models such as ChatGPT, Claude, Gemini, and Llama and applying them within agentic AI systems. Experience creating, deploying, and integrating Model Context Protocol implementations into agentic AI solutions is also required.

Experience with AI frameworks such as Google ADK, LangGraph, LangChain, AutoGen, CrewAI, and N8N is relevant for building intelligent workflows and autonomous or semi-autonomous AI solutions.

Preferred Skills

Experience with Java and the Spring ecosystem can complement strong Python expertise. Professionals who have worked on full-stack applications using Angular, React, Node.js, and TypeScript may be well suited to integrated AI engineering projects.

Strong experience with API development, microservices, event-driven systems, managed services, and existing enterprise platforms is valuable. Candidates should also understand application resiliency and security principles for complex AI projects.

Experience with Docker and OpenShift is important for modern application deployment. Knowledge of Git, CI/CD, comprehensive testing, code reviews, Agile methodologies, and operational stability practices will strengthen an application.

Education

The supplied job description does not explicitly state a specific degree requirement. Candidates should therefore provide their actual educational qualifications or equivalent professional experience without claiming an education requirement that is not stated in the source.

Experience

The position requires 12+ years of experience in the data domain, with deep hands-on experience engineering scalable enterprise solutions and handling large-volume data. The role is described as an individual contributor position, so applicants should emphasize technical depth, architecture experience, and direct implementation rather than relying only on people-management experience.

Relevant experience includes data engineering, ETL development, Python programming, enterprise databases, AI engineering, API development, machine learning, LLM integration, agentic AI, distributed systems, and modern software architecture.

Soft Skills

Strong analytical and problem-solving ability is important for handling complex data and AI engineering challenges. Clear communication and interpersonal skills are needed to work effectively with data analysts, technical teams, and other stakeholders.

Candidates should demonstrate ownership, technical curiosity, collaboration, attention to quality, and the ability to work through complex problems independently. A continuous-learning mindset is especially useful because the role involves rapidly evolving AI and data technologies.

Benefits of Working in this Role

This role provides an opportunity to work on enterprise-scale data and AI engineering problems while combining traditional data engineering with modern generative AI and agentic AI technologies. The position offers exposure to large-volume data processing, AI models, machine learning frameworks, modern application architectures, API engineering, DevOps, and container platforms.

The individual contributor structure also allows experienced engineers to remain deeply involved in technical design and implementation while influencing enterprise engineering practices through their expertise.

Work Mode

This is a hybrid full-time position based in Pune, Maharashtra, India.

Location

The job is located in Pune, Maharashtra, India. Candidates should be prepared to work within the stated hybrid arrangement.

Who Should Apply

This opportunity is designed for senior data engineers, AI engineers, data platform engineers, Python engineering leads, machine learning engineers, and experienced software professionals with 12+ years of data-domain experience.

Strong candidates should be able to demonstrate hands-on delivery of large-scale data solutions and practical experience across Python, ETL, SQL, databases, APIs, AI engineering, LLMs, machine learning, and modern application architecture.

Career Growth

This Senior Vice President role can strengthen a career in enterprise data engineering, AI engineering, platform architecture, machine learning systems, and technical leadership. Experience across agentic AI, LLM integration, data platforms, API-first architecture, microservices, and large-scale data processing can support future opportunities in principal engineering, AI architecture, data platform architecture, and enterprise technology strategy.

Application Advice

Customize your resume around measurable technical contributions rather than listing technologies without context. Highlight large-volume data projects, ETL modernization, Python pipeline development, database optimization, and scalable enterprise solutions.

Clearly describe experience with FastAPI, Flask, PySpark, Oracle, SQL, PL/SQL, PostgreSQL, MongoDB, and any Java or Spring-based applications. For AI-focused work, mention practical projects involving chunking, embeddings, prompt engineering, RAG or agentic workflows, LLM integration, model fine-tuning, and MCP implementations where applicable.

Also highlight experience with LangGraph, LangChain, Google ADK, AutoGen, CrewAI, TensorFlow, PyTorch, Docker, OpenShift, Git, CI/CD, microservices, and event-driven architectures. Provide concrete examples of system design, API engineering, security, testing, reliability, and production delivery.