Remote Python Fullstack Engineer

Time zones: EST (UTC -5), CST (UTC -6), MST (UTC -7), PST (UTC -8), ART (UTC -3), UTC -4, UTC -4:30, UTC -3, UTC -2, SBT (UTC +11), GMT (UTC +0), CET (UTC +1), EET (UTC +2), MSK (UTC +3), AST (UTC -4), FKST (UTC -3), NST (UTC -3:30), CEST (UTC +2), BST (UTC +1), JST (UTC +9), CST (UTC +8), WIB (UTC +7), MMT (UTC +6:30), BST (UTC +6), NPT (UTC +5:45), IST (UTC +5:30), UZT (UTC +5), IRDT (UTC +4:30), GST (UTC +4), LINT (UTC +14), TOT (UTC +13), CHAST (UTC +12:45), LHST (UTC +10:30), AEST (UTC +10), ACST (UTC +9:30), ACWST (UTC +8:45), MART (UTC -9:30), NUT (UTC -11)

 

 

CloudDevs is helping world-class, venture-backed AI startups find talented AI full-stack developers. You will be employed by one of these startups and play an integral role in their early-stage growth.

 

 

Minimum qualifications:

 

Proven track record of shipping software and successfully released apps (please include names and links on your resume)

 5+ yrs of commercial experience using React or Vue or any JS framework on the front-end and major frameworks like python/ go/Rails/elixir/java/flask/NodeJS on the backend. 

Bachelor’s degree in Computer Science or equivalent practical experience

7+ years of work experience as a software engineer or relevant experience

Strong and confident communicator in English

Strong problem solver

Comfortable with collaboration and open communication across distributed teams

 

 

 work:

 

Design, develop, and implement custom latest generative LLMs (e.g. models similar to GPT or ChatGPT or GPT4) and discriminative LLMs (e.g. models similar to BERT).

create wrapper apps based on ChatGTP and other LLMs 

Design, develop, and implement systems for AI alignment, Reinforcement Learning with Human Feedback (RLHF) instruction models, and AI guardrails.

Conduct rigorous tests to evaluate LLMs across standardized performance benchmarks and custom evaluations.

Employ cutting edge Natural Language Processing (NLP) and Machine Learning (ML) techniques to solve complex natural language problems.

Translate technical findings into clear, actionable insights for a non-technical audience.

Here’s what you need:

 

Experience with generative LLM fine-tuning and prompt engineering

Experience with deep learning frameworks

Experience with Hugging Face Transformers and other open source NLP/NLG modules