Back
Strong employer brand, metro location, mid-level generalist Data Scientist title, and broad GenAI skill demands raise competition.
Requires specialized LLM and generative AI expertise, making background transferability across unrelated industries low.
Many mandatory specialized GenAI tools, ML finetuning techniques, cloud and deployment skills imply strict technical filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement advanced Large Language Model (LLM)-based generative AI solutions end-to-end.
Develop and maintain code libraries, tools, and frameworks to support generative AI projects, including participation in code reviews to ensure high code quality.
Collaborate with cross-functional teams throughout the software development lifecycle to align roadmaps and integrate solutions into core systems.
Hands-on experience with Natural Language Processing (NLP) use cases and/or Computer Vision and Audio AI techniques.
Proficiency with SaaS LLMs and generative AI tools including Lang chain, llama index, prompt engineering, and experience with Azure OpenAI, Google Vertex AI, or AWS Bedrock.
Experience with open-source LLM frameworks such as TensorFlow/PyTorch and huggingface, including techniques like LLM fine-tuning and GPU utilization.
Proficiency in Python programming, Docker, frameworks like FastAPI/Django/Flask, Git, and practical experience with cloud platforms (Azure, AWS, or GCP); cloud certification preferred.
Experienced working with scalable generative AI architectures and LLM deployment in production environments.
Technically skilled in integrating LLM and generative AI technologies into cross-functional projects with an emphasis on maintainability and quality.
Familiar with research and application of latest generative AI trends, able to bridge experimental solutions with practical software development demands.