Machine Learning Engineer
hace 3 semanas
About FactoredFactored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine‑Learning engineers globally. We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.Machine Learning Engineer – Retrieval‑Augmented Generation (RAG)ResponsibilitiesDesign, develop, and optimize Retrieval‑Augmented Generation (RAG) models that integrate retrieval‑based and generation‑based approaches to solve complex, real‑world problems for high‑profile clients.Improve the performance of RAG models through cutting‑edge algorithms, innovative techniques, and model fine‑tuning.Collaborate with client data and engineering teams to establish and build robust machine learning infrastructure to meet project goals.Work closely with leadership teams from clients to identify and leverage AI/ML opportunities that can provide transformative solutions.Fine‑tune and adapt large language models (LLMs) for specific tasks and domains within the RAG framework.Partner with cross‑functional client teams to deploy RAG models into production environments, ensuring seamless integration and long‑term success.Apply advanced machine learning techniques, including LLMs, to develop effective AI solutions tailored to client needs.Write clean, maintainable, and scalable code, ensuring all development is well‑documented and testable.Prioritize user experience and customer needs in all product development efforts.Design and develop frameworks for GenAI products, such as search interfaces, chatbots, and summarization tools.Build and implement machine learning models and algorithms that directly contribute to client growth and success through innovative, AI‑driven solutions.Provide technical leadership in identifying and evaluating AI/ML opportunities that empower clients to deliver exceptional solutions.QualificationsBachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.5+ years of hands‑on experience developing and deploying machine learning models in production environments.4+ years of experience with production NLP and deep learning models using frameworks like PyTorch and TensorFlow.At least 1+ year of experience with Retrieval‑Augmented Generation (RAG) and other advanced techniques to optimize model performance.Proven experience writing production‑level code, with strong proficiency in Python.Expertise in working with large language models (LLMs) such as GPT, Gemini, and Claude, along with proficiency in LLM frameworks like LangChain.Strong understanding of prompting techniques, and the trade‑offs between prompting and fine‑tuning.Experience with cloud platforms such as AWS or GCP (AWS preferred), or equivalent on‑premise platforms. #J-18808-Ljbffr
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