Lead AI Engineer

What you’ll do:

Implement and maintain the infrastructure for end-to-end machine learning workflows, including data collection, model training, and deployment in production environments. Manage the lifecycle of large language models (LLMs), encompassing training, evaluation, deployment, and monitoring. Evaluate the performance of various machine learning models using appropriate metrics and statistical tests, making recommendations on model selection based on performance. Fine-tune and enhance existing Retrieval Augmented Generation (RAG) and LLM models to improve their performance and adaptability. Design and optimize prompts to effectively guide language model behavior and manage prompt flow in conversational or task-oriented contexts (Prompt Engineering). Provide guidelines and strategies for improving the accuracy of machine learning models. Advise on optimizing token usage for cost savings and performance improvements, including strategies for efficient data preprocessing, model architecture design, and deployment. Leverage Azure AI technologies, such as Azure Machine Learning and Cognitive Services, for developing, deploying, and managing AI solutions. Possess knowledge and experience in deploying models and applications in cloud environments, particularly Azure.

Qualifications:

• Bachelor’s degree in computer science, Data Science, or a related field. An advanced degree is preferred.
• Overall 10+ years of experience, with 5 to 7 years of proven experience as an AI Engineer or in a similar role.

Skills:

• Strong knowledge of machine learning algorithms and principles, and Prompt engineering.
• Proficiency with Azure AI technologies, including Azure OpenAI.
• Experience with MLOps, LLM Ops, model evaluation techniques, and Retrieval Augmented Generation.
• Strong programming skills, preferably in Python. Experience with C# and .NET is a plus.
• Familiarity with models like LAMA and Mistral.
• Excellent problem-solving abilities and attention to detail.
• Strong communication skills and the ability to explain complex concepts to non-technical stakeholders.