Data Engineer (Python/AI) - Assistant Vice President

Citi; New York (NY); Hybrid; USD 100,000 - 130,000; Full time

Job summary

Citi is looking for a Data Engineer (Python/AI) - Assistant Vice President to join their team in New York. The candidate must have extensive experience in developing NLP pipelines and data processing frameworks. This role demands strong programming skills in Python and proficiency in technologies such as PySpark and Flask. A hybrid work model is available, allowing for a flexible work environment.

The ideal candidate will collaborate on CI/CD practices and engage in data ingestion and transformation projects, while ensuring effective integration with other services.

Qualifications

• 8+ years of hands-on Python programming experience.
• Strong fundamentals in OOP and design patterns.
• Experience with Redis and JSON parsing libraries.

Responsibilities

• Develop and optimize ETL/data processing jobs using PySpark, Pandas.
• Build and maintain NLP pipelines using Flair and BERT.
• Integrate CI/CD with GitHub and deployment pipelines.

Skills

Python programming experience

NLP libraries (Flair, BERT)

PySpark

Flask for API development

Redis

Tools

MLflow

GitHub

Docker

Job description

Data Engineer (Python/AI) - Assistant Vice President

Job Req Id: 26943344

Location(s): Pune, Maharashtra, India

Job Type: Hybrid

Posted: May. 05, 2026

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Job Overview

Role Summary We are looking for a mid-level Python Developer with combined experience in Data Engineering and AI/NLP engineering. The candidate will build NLP pipelines using libraries such as Flair, BERT, and LLM frameworks, and will also work on large-scale data processing using PySpark, Pandas, and related data tools. The role includes developing APIs, integrating with platform services, and supporting CI/CD deployments using GitHub and LightSpeed Enterprise.

Key Responsibilities

• Develop and optimize ETL/data processing jobs using PySpark, Pandas, PyArrow, and related libraries.
• Work with Parquet files using FastParquet or pyarrow.parquet for efficient data processing.
• Implement data parsing and serialization using json, ujson, or orjson for high-performance JSON handling.
• Build and maintain NLP pipelines using Flair, BERT, and LLM-based models.
• Develop scalable ingestion and data transformation pipelines for AI and analytics use cases.
• Build and maintain Flask-based APIs for model inference and service integrations.
• Use regular expressions for text cleaning, parsing, and NLP preprocessing.
• Integrate caching and fast lookups using Redis.
• Manage and deploy ML models using MLflow for tracking and versioning.
• Support CI/CD workflows using GitHub, LightSpeed Enterprise, and deployment pipelines.
• Create and maintain Autosys JILs for job scheduling and automation.
• Use basic Linux commands for troubleshooting, operations, and deployment tasks.
• Monitor application and system health using ITRS Geneos.
• Write unit tests and improve automation test coverage (PyTest/unittest).
• Work with REST APIs, microservices, and basic shell scripting.
• Work with cloud services (ECS), including boto3.

Required Skills

• 8+ years of hands‑on Python programming experience.
• Strong fundamentals in Python, OOP, and design patterns.
• Experience with NLP libraries such as Flair, BERT, HuggingFace Transformers, or similar.
• Solid experience with PySpark, Pandas, PyArrow, and distributed data pipelines.
• Proficient in working with Parquet using FastParquet or pyarrow.parquet.
• Familiarity with fast JSON parsing libraries (json, ujson, orjson).
• Experience building APIs using Flask (FastAPI is a plus).
• Experience with MLflow for model tracking and deployment.
• Good understanding of CI/CD practices and Git workflows.
• Experience working with Redis or similar in‑memory stores.
• Experience with Autosys JILs for job scheduling.
• Comfortable with Linux command line and shell scripting.
• Strong debugging, problem‑solving, and teamwork skills.
• Exposure to cloud services; AWS boto3 experience is an asset.

Nice‑to‑Have

• Experience with Polars or Dask for high‑performance data processing.
• Experience with PyTorch or TensorFlow for model training.
• Experience with Docker, Kubernetes, or containerized deployments.
• Experience with monitoring tools such as ITRS Geneos.
• Experience with FastAPI, Airflow, or Prefect.

Job Family Group:

Technology

Job Family:

Applications Development

Time Type:

Full time

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