Lead Machine Learning Engineer- MLOps, VertexAI, LLMs, GenAI

Job Description

Position - Lead Machine Learning Engineer- MLOps, VertexAI, LLMs, GenAI, ML Model Management Role Overview UPS Data Science and Machine Learning team is seeking a highly skilled and experienced Lead Machine Learning Engineer to manage our AI, ML, GenAI application focused on Cross Border logistics. This position leverages continuous integration and deployment of the best practices, including test automation and monitoring, to ensure successful deployment of optimal ML models and analytical systems. You will be responsible for the end-to-end lifecycle of AI models, from experimentation and fine-tuning to deployment and management in production. A strong background in prompt engineering and practical experience with either Google Cloud's Vertex AI platform is essential for this role. You will also provide technical leadership and mentorship to other members of the AI/ML team. Key Responsibilities Lead the development and deployment of generative AI solutions utilizing LLMs, SLMs, and FMs for various applications (e.g., content generation, chatbots, summarization, code generation, etc.). Architect and implement robust and scalable infrastructure for training, fine-tuning, and serving large-scale AI models, leveraging either Vertex AI. Drive the fine-tuning and adaptation of pre-trained models using proprietary data to achieve state-of-the-art performance on specific tasks. Develop and implement effective prompt engineering strategies to elicit desired outputs and control the behavior of generative models. Manage the lifecycle of deployed models , production support, including monitoring performance, identifying areas for improvement, and implementing necessary updates or retraining. Collaborate closely with cross-functional teams (e.g., product, engineering, research) to understand business requirements and translate them into technical solutions. Provide technical leadership and mentorship to junior machine learning engineers, fostering a culture of learning and innovation. Ensure the responsible and ethical development and deployment of AI models , considering factors such as bias, fairness, and privacy. Stay up to date with latest advancements in generative AI, LLMs, and related technologies, and evaluate their potential application within the company. Document technical designs, implementation details, and deployment processes. Troubleshoot and resolve issues related to model performance and deployment. Required Skills and Experience: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Minimum of 5-8 years of hands-on experience in building, deploying, and managing machine learning models in a production environment. Demonstrable experience in managing, deploying, and fine-tuning large language models (LLMs), small language models (SLMs), and foundation models (FMs). Significant hands-on experience with prompt engineering techniques for various generative AI tasks. Proven experience working with either Google Cloud's Vertex AI platform platform . including experience with their respective model registries, deployment tools, and MLOps features. Strong programming skills in Python and experience with relevant machine learning libraries (e.g., TensorFlow, PyTorch, Transformers). Experience with cloud computing platforms (beyond Vertex AI is a plus, e.g. Azure). Solid understanding of machine learning principles, deep learning architectures, and evaluation metrics. Excellent problem-solving, analytical, and communication skills. Ability to work independently and as part of a collaborative team. Experience with MLOps practices and tools for continuous integration and continuous delivery (CI/CD) of ML models is highly desirable. Experience with version control systems (e.g., Git). Bonus Points: Experience with model governance frameworks and implementing ethical AI practices. Experience with specific generative AI use cases relevant to Logistics industry. Publications or contributions to open-source projects, technical blogs, or industry conferences are considered a plus Familiarity with data engineering pipelines and tools. Familiarity with emerging trends in generative AI, reinforcement learning from human feedback (RLHF), and federated learning approaches.