Machine Learning Engineer (LLM/RAG)

At DigitalTwin Technology, we are at the forefront of transforming industries through our groundbreaking digital twin solutions. Our mission is to revolutionize how organizations interact with data by creating intelligent, responsive digital twins that enhance decision-making, optimize operations, and drive innovation. We are looking for a highly skilled Senior Machine Learning Engineer to join our dynamic team and contribute to the development of advanced Retrieval-Augmented Generation (RAG) models and large language models, utilizing the latest techniques in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG).

As a Senior Machine Learning Engineer specializing in Conversational AI, you will be instrumental in designing, developing, and deploying cutting-edge models that power our digital twin solutions. You will work closely with cross-functional teams, including product managers, software engineers, and data scientists, to seamlessly integrate machine learning capabilities into our products, ensuring they meet the highest standards of performance and usability. Join us in shaping the future of intelligent systems and making a significant impact on industries worldwide.

• Design, Develop, and Optimize RAG Models: Create and refine Retrieval-Augmented Generation (RAG) models to enhance information retrieval and generation across various industries, ensuring accurate and contextually relevant responses in our conversational AI systems.
• Utilize Advanced Indexing Techniques: Leverage vector databases and cutting-edge indexing methods to efficiently store, manage, and retrieve relevant data, optimizing performance for diverse conversational contexts across different sectors.
• Fine-Tune Large Language Models: Customize and optimize large language models, such as GPT (Generative Pre-trained Transformer), for specific applications across multiple industries, tailoring their accuracy and relevance to meet varied use cases.
• Implement Advanced NLP, NLU, and NLG Techniques: Experiment with and apply state-of-the-art Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) methods to continually enhance the capabilities and performance of our AI-driven solutions.
• Collaborate on System Integration: Work closely with software engineers to seamlessly integrate machine learning models into production environments, ensuring systems are scalable, reliable, and high-performing across a wide range of industries.
• Stay Ahead of Industry Advancements: Engage in ongoing research and monitor the latest developments in machine learning, NLP, and conversational AI to drive continuous innovation and maintain a competitive edge, regardless of the industry.
• Provide Technical Leadership and Mentorship: Offer guidance and support to junior machine learning team members, fostering a culture of continuous learning, professional development, and collaboration within a diverse, multi-industry environment.

• Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, or related field. Advanced degree preferred.
• 5+ years of experience in machine learning engineering, with a focus on building and deploying conversational AI solutions.
• Proven expertise in developing RAG models, working with vector databases, and fine-tuning large language models.
• Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or JAX.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Solid understanding of NLP fundamentals and experience with NLU/NLG techniques such as sentiment analysis, entity recognition, and text generation.
• Excellent problem-solving abilities and a pragmatic approach to building scalable and robust machine learning systems.
• Strong communication skills with the ability to collaborate effectively with cross-functional teams and articulate complex technical concepts to non-technical stakeholders.

• Competitive salary and benefits package.
• Opportunities for professional development and growth.
• A supportive and dynamic work environment.
• Flexible working hours and home office opportunities.