Senior Machine Learning Engineer (RAGs)

We are seeking a skilled Senior Machine Learning Engineer to join our team, with a specialized focus on Retrieval-Augmented Generation (RAG) models. The ideal candidate will have experience in designing, developing, and deploying advanced machine learning models that integrate retrieval and generation capabilities to create powerful AI-driven applications.

Functional Responsibilities:

• Design, develop, and optimize Retrieval-Augmented Generation (RAG) models that integrate retrieval-based and generation-based approaches to solve complex, real-world problems for our 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 our 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.

Qualifications:

• Bachelor’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.