Why This Role Stands Out
Advance your career with this exciting opportunity to leverage Python, PySpark, NLP, and MLOps within the dynamic banking payments domain. You'll thrive in this role if you are a mid-senior engineer eager to build and deploy cutting-edge machine learning solutions, contributing to impactful projects in a reputable company. Embrace the chance to expand your technical expertise and make a significant contribution by applying today.
Quick Overview
Job Description
Role: Python / PySpark / NLP / MLOps Engineer
Location: Pittsburgh, PA/Lake Mary, FL/NYC, NY – (Onsite/Hybrid – 3 days onsite per week)
Type: Long Term Contract
Industry: Banking Payments Domain
JD:
Client is seeking an experienced **Python / PySpark / NLP / MLOps Engineer** to join our technology team. The ideal candidate will have strong hands-on experience in Python development, distributed data processing using PySpark, Natural Language Processing (NLP), and productionizing machine learning solutions through MLOps practices.
The ideal candidate is a hands-on engineer who can work across the complete lifecycle—from data preparation and PySpark processing to NLP/ML model development and production deployment using MLOps practices.
Key Responsibilities:
- Develop scalable and production-ready applications using Python.
- Build and optimize large-scale data processing pipelines using Apache Spark / PySpark.
- Develop NLP solutions for processing and extracting insights from structured and unstructured data.
- Develop, train, validate, deploy, and monitor machine learning models in production environments.
- Implement MLOps best practices across the ML lifecycle, including model versioning, experiment tracking, CI/CD, deployment, monitoring, and model governance.
- Work with data scientists to convert machine learning prototypes into scalable production solutions.
- Design and implement data pipelines supporting ML/NLP workloads.
- Optimize PySpark jobs for performance, scalability, and reliability.
- Build reusable Python libraries, APIs, and automation frameworks.
- Implement automated testing, deployment, and monitoring for ML applications.
- Collaborate with engineering and business teams to understand requirements and deliver robust solutions.
- Troubleshoot production issues and continuously improve system performance and reliability.
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