Data Engineer - Python, AI

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.

• Develop and optimize ETL/data processing jobs using PySpark, Pandas, PyArrow, and related libraries.
• 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.

• 10–12 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.
• 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.

• 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.

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