Automation QA with AI

Jersey City, NJ Jersey City NJ 07399

Date : May-19-25

Automation QA with AI

Jersey City, NJ

May-19-25

Work Authorization

US Citizen
GC
H1B
GC EAD, TN EAD, H4 EAD, L2 EAD

Preferred Employment

Corp-Corp
W2-Permanent
W2-Contract
1099-Contract
Contract to Hire

Job Details

Experience

:

Architect, Senior

Rate/Salary ($)

:

Market

Duration

:

6 months

Sp. Area

:

AI, ML, NLP, Data Science

Sp. Skills

:

[AI] Artificial Intelligence

Permanent Direct Hire

Consulting / Contract

Required Skills :

Artificial Intelligence, DevOps, Machine learning, Continuous deployment, JAVA, Natural Language Processing, Python, Scala, Selenium, Windows Azure

Preferred Skills :

Domain :

Financial

Work Authorization

US Citizen
GC
GC EAD, TN EAD, H4 EAD, L2 EAD
H1B

Preferred Employment

Corp-Corp
W2-Permanent
W2-Contract
1099-Contract
Contract to Hire

Job Details

Experience

:

Architect, Senior

Rate/Salary ($)

:

Market

Duration

:

6 months

Sp. Area

:

AI, ML, NLP, Data Science

Sp. Skills

:

[AI] Artificial Intelligence

Permanent Direct Hire

Consulting / Contract

Required Skills :

Artificial Intelligence, DevOps, Machine learning, Continuous deployment, JAVA, Natural Language Processing, Python, Scala, Selenium, Windows Azure

Preferred Skills :

Domain : Financial

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Synkriom
Edison, NJ
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Job Description :

Job Description:

Experience in QA Engineering, AI/ML, and automation frameworks
Proven experience in Designing and implementing AI-based test automation frameworks.
NLP, LLMs (e.g., GPT-4, BERT), and data annotation techniques.
Tools such as TensorFlow, PyTorch, Hugging Face, or equivalent.
Strong programming background: Python, Java, or Scala.
Experience with test automation tools like Selenium, Cypress, TestNG, or equivalent.
Solid understanding of payment systems, financial services regulations, and transactional risk analysis
Knowledge of CI/CD pipelines, DevOps, and tools like Jenkins, GitHub Actions, Azure DevOps, etc.
Experience working with structured/unstructured data, data lakes, and data pipelines.
Demonstrated success in delivering greenfield AI/ML initiatives from concept to scale.

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