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Deployed Engineer (Charlotte)

LangChain is seeking a dedicated Deployed Engineer to join our growing team in Charlotte, NC. This role is critical for transitioning our cutting-edge AI models into reliable, production-ready systems that serve real-world customers. Key responsibilities include designing and implementing robust ML pipelines, optimizing cloud infrastructure (AWS/GCP), and collaborating with data scientists to scale prototypes. The ideal candidate possesses strong backend development skills, proficiency in Python, and deep experience deploying complex software solutions in a remote setting. You will be instrumental in ensuring our AI products maintain high availability, scalability, and low latency as we expand our footprint.

langchain Charlotte, NC (Remote), United States

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)

Preparing

1800

Question set

250

Duration

Permanent

Focus areas

Backend DevelopmentCloud InfrastructurePythonMachine Learning Pipelines

People Also Ask (FAQs)

What is the last date to apply for Deployed Engineer (Charlotte)?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Deployed Engineer (Charlotte)?

Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.

Is there negative marking in the ashby test?

The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.

What is the passing marks for this test?

The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.

How can I prepare for the Deployed Engineer (Charlotte) test?

Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Backend Development, Cloud Infrastructure, Python, Machine Learning Pipelines.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

Prepare for this test

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