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Tier-1 employer and metro location increase competition, but seniority and specialized NLP skills moderate applicant density.
Core data engineering and NLP skills are transferable, though some finance-specific tooling adds moderate domain bias.
Explicit 10–12 years requirement plus many mandatory technologies increases filtering strictness.
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Develop and optimize large-scale ETL and data processing pipelines using PySpark, Pandas, and related data engineering tools.
Build and maintain NLP pipelines and services using Flair, BERT, LLM models, and develop Flask-based APIs for model inference and platform integration.
Support deployment and operational tasks including ML model management with MLflow, CI/CD workflows, job scheduling with Autosys, and monitoring application/system health.
10–12 years of hands-on Python programming experience with strong fundamentals in OOP and design patterns.
Experience working with NLP libraries such as Flair, BERT, HuggingFace Transformers, or similar.
Proficiency in data engineering tools including PySpark, Pandas, PyArrow, and building scalable distributed data pipelines.
Experience with APIs development using Flask, MLflow for model tracking, CI/CD using GitHub, Redis, Autosys JILs, Linux command line, and basic shell scripting.
Experienced in combining data engineering and AI/NLP engineering to build production-level NLP pipelines and scalable data solutions.
Familiar with operational deployment practices including CI/CD pipelines, job scheduling, cloud services (AWS boto3), and application monitoring.
Comfortable handling end-to-end data workflows from ingestion, model inference APIs, to deployment and monitoring in fast-paced technology environments.