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Strong employer brand, metro location, generalist Data Engineer title, and broad skillset increase applicant competition.
Technical data and NLP skills are transferable, but banking tooling and seniority require some domain-specific fit.
Explicit 10–12 years plus many mandatory technical stack items and domain tools makes shortlisting highly strict.
Develop and optimize large-scale data processing and ETL jobs using PySpark, Pandas, and PyArrow for AI and analytics applications.
Build and maintain NLP pipelines leveraging Flair, BERT, HuggingFace Transformers, and LLM frameworks.
Develop and manage scalable Flask-based APIs, ML model deployment with MLflow, and support CI/CD and job scheduling workflows.
10–12 years of hands-on Python programming experience with strong OOP and design patterns knowledge.
Experience with NLP libraries (Flair, BERT, HuggingFace Transformers) and distributed data tools (PySpark, Pandas, PyArrow).
Experience in building Flask APIs, MLflow deployment, CI/CD practices with Git workflows, and Autosys JILs for scheduling.
Proficiency with Linux command line and shell scripting; familiarity with Redis or similar in-memory stores.
Mid-level to senior Python developer with combined expertise in data engineering and AI/NLP pipeline development.
Experience applying advanced NLP techniques and building distributed data ingestion and transformation pipelines at scale.
Proven ability to integrate ML models into production environments including API development, job scheduling, and CI/CD automation.