About Quadsci

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Responsibilities

Design, configure and optimize the GenAI-tech stack including: LLM, Vector DB, Encoder / Decoder, prompt framework (ex. DSPy) and supporting cloud compute and service resources.
Design and implement RAG pipelines that enhance generative AI models by integrating external data sources.
Architect and engineer efficient retrieval systems that can fetch relevant data from databases, knowledge graphs, or external APIs to augment AI-generated responses.
Develop prompting pipelines that leverage context and retrieved information to generate accurate and contextually relevant responses.
Collaborate with machine learning engineers to implement advanced techniques such as vector search, semantic search, and embeddings to improve data retrieval accuracy.
Build and maintain robust pipelines for data retrieval, preprocessing, and integration into the generation process.
Implement automated testing frameworks to validate the performance of RAG and prompting pipelines.
Ensure that the retrieval and generation pipelines are scalable, reliable, and maintainable.
Collaborate with cross-functional teams including UX/UI designers, product managers, and DevOps engineers to deliver high-quality products.
Collaborate with DataML Engineers, Integration Engineers & GenAI Engineers for customer-specific deployments & configurations

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
A strong foundation in software engineering principles is essential.
Additional coursework or certifications in AI/ML or data science is a plus.
5+ years of professional experience in complex systems engineering, with a strong focus on AI-driven applications.
Proven experience in integrating and deploying machine learning models, particularly in generative AI (e.g., GPT, GANs, VAE, etc.).
Demonstrated experience in architecting, engineering and deploying RAG pipelines for generative models and complex prompting systems.
Familiarity with Python-based APIs