Junior Engineer
Ford
We are looking for an experienced AI/ML Engineer with over 4 years in the field of AI/ML to become a part of our team. The perfect candidate should possess a solid background in traditional machine learning, Generative AI, and have experience fine-tuning large language models (LLMs). This role involves a significant contribution to designing scalable AI solutions that enhance business operations. You will collaborate with cross-functional teams to develop AI-driven systems that offer substantial operational benefits. A strong understanding of Retrieval-Augmented Generation (RAG) and proficiency with databases are essential for this role.
Experience: 4 to 10 years in AI/ML, with a strong emphasis on traditional machine learning and deploying AI/ML models in production environments. Generative AI: Demonstrated experience in developing applications based on Generative AI for real-world users. Machine Learning: Solid foundation in machine learning techniques, including both deep learning and traditional ML methods. Language Models: Experience in fine-tuning large language models (LLMs) and applying Retrieval-Augmented Generation (RAG) for business purposes. Data Management: Proficiency with databases, including SQL and NoSQL, and managing large datasets in production settings. Technical Expertise: Expertise in AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, and programming languages like Python. MLOps: Knowledge of MLOps practices for deploying, monitoring, and maintaining machine learning models at scale. Cloud and DevOps: Familiarity with cloud platforms such as AWS, GCP, or Azure, and tools like Docker and Kubernetes. Communication: Strong interpersonal and communication skills, capable of working effectively with both technical and non-technical teams. Design, develop, and deploy AI applications suitable for production that seamlessly integrate into business workflows, utilizing Generative AI models like GPT and Retrieval-Augmented Generation (RAG) frameworks. Lead the complete machine learning lifecycle, encompassing data collection, preprocessing, model development, fine-tuning, deployment, and ongoing monitoring. Collaborate with product and engineering teams to embed AI/ML solutions into workflows, driving automation and enhancing productivity in professional services. Apply traditional machine learning techniques, such as regression, classification, and clustering, to improve existing solutions and create new ones. Fine-tune large language models (LLMs) and implement RAG strategies to deliver real-time, data-driven insights and support decision-making processes. Work with databases to underpin data-driven AI models, ensuring efficient data storage, access, and preprocessing of large datasets. Continuously optimize model performance to maintain scalability, reliability, and efficiency in a cloud-based environment. Monitor and sustain production models, employing feedback loops and A/B testing to enhance performance. Stay current with the latest advancements in AI/ML and integrate them into AI solutions. Mentor and guide junior data scientists, fostering a collaborative and innovative culture.
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