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Senior specialized role at a Tier-1 firm but popular Data Engineer title creates moderate applicant density.
Requires deep data engineering and NLP experience, so candidates from other industries have limited transferability.
Explicit 10-12 years plus multiple mandatory technical skills (PySpark, NLP, MLflow, Autosys), so filters are strict.
Develop and optimize large-scale ETL and data processing pipelines using PySpark, Pandas, and related tools for AI and analytics use cases.
Build and maintain NLP pipelines leveraging libraries such as Flair, BERT, HuggingFace Transformers, and integrate ML models with Flask-based APIs including model deployment and versioning with MLflow.
Support and manage CI/CD workflows, automate job scheduling with Autosys JILs, and perform operational tasks using Linux, monitoring tools, and cloud services integrations.
10–12 years of hands-on Python programming experience with strong fundamentals in OOP and design patterns.
Experience with NLP libraries (Flair, BERT, HuggingFace Transformers) and large-scale distributed data processing tools (PySpark, Pandas, PyArrow).
Proficiency in building and maintaining APIs using Flask and working knowledge of MLflow, Redis, Autosys JILs, Linux command line, and shell scripting.
Work Experience Required: 10-12 years in Python programming and data engineering; Notice period: Not explicitly mentioned in the JD.
Experienced in both data engineering and AI/NLP engineering, comfortable working with scalable ingestion and transformation pipelines for AI applications.
Demonstrates operational ownership through managing deployments, CI/CD workflows, and job scheduling automation in complex environments.
Familiar with cloud service integrations (AWS boto3), monitoring solutions (ITRS Geneos), and capable of troubleshooting in Linux-based production systems.