Senior Machine Learning Engineer – RAGs
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Job Description
Senior Machine Learning Engineer developing production RAG models and GenAI products for Factored’s clients. Building AI infrastructure and deploying LLM solutions with LATAM-based talent.
Responsibilities:
- Design, develop, and optimize Retrieval-Augmented Generation (RAG) models for complex, real-world client problems.
- Improve RAG model performance through advanced algorithms, innovative techniques, and fine-tuning.
- Collaborate with client Data and Engineering teams to build robust machine learning infrastructure.
- Work with client leadership to identify and leverage transformative AI/ML opportunities.
- Fine-tune and adapt large language models for specific tasks and domains within the RAG framework.
- Deploy RAG models into production environments and ensure seamless integration and long-term success.
- Apply advanced machine learning techniques, including LLMs, to develop client-tailored AI solutions.
- Write clean, maintainable, scalable, documented, and testable code.
- Prioritize user experience and customer needs in product development.
- Design and develop GenAI product frameworks, including search interfaces, chatbots, and summarization tools.
- Build and implement machine learning models and algorithms supporting client growth and success.
- Provide technical leadership in identifying and evaluating AI/ML opportunities.
Requirements:
- 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.
Benefits:
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance culture.
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