
Application Management Services LLC
Hybrid in New York, New York • 5d ago
Easy Apply
Contract
Depends on Experience
363 results (20 new)

Application Management Services LLC
Hybrid in New York, New York • 5d ago
Easy Apply
Contract
Depends on Experience












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New York, New York • 26d ago
Full-time
USD 158,000.00 - 170,000.00 per year

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Hybrid in New York, New York • 7d ago
Easy Apply
Contract, Third Party
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Disney Entertainment and ESPN Product & Technology Careers
New York, New York • Today
Full-time
USD 117,500.00 - 157,500.00 per year








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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