Python Developer - NLP, ML, Gen AI - Assistant Vice President

Role Summary

We are looking for a mid-level Python Developer - NLP, ML, Gen AI 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 Git Hub and Light Speed Enterprise.

• Develop and optimize ETL/data processing jobs using PySpark, Pandas, PyArrow, and related libraries.
• Work with Parquet files using Fast Parquet 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 Git Hub, Light Speed 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 boto
  3.

• 3–5 years of hands‑on Python programming experience.
• Strong fundamentals in Python, OOP, and design patterns.
• Experience with NLP libraries such as Flair, BERT, Hugging Face Transformers, or similar.
• Solid experience with PySpark, Pandas, PyArrow, and distributed data pipelines.
• Proficient in working with Parquet using Fast Parquet 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.
• Solid 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 Tensor Flow for model training.
• Experience with Docker, Kubernetes, or containerized deployments.
• Experience with monitoring tools such as ITRS Geneos.
• Experience with FastAPI, Airflow, or Prefect.