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. 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
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. 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
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Job Family:
Applications Development
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Time Type:
Full time
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Most Relevant Skills
Please see the requirements listed above.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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