Source description
About the role
As a Principal AI Engineer, you will play an instrumental and influential role in driving Generative AI vision, strategy, and architecture. Your key responsibilities include: - Architecting, building, maintaining, and improving new and existing suite of GenAI applications and their underlying systems. - Automating machine learning pipelines, monitoring performance and costs, and optimizing models using techniques such as LoRA/QLoRA. - Establishing reusable frameworks to streamline model building, deployment, and monitoring. - Incorporating comprehensive monitoring, logging, tracing, and alerting mechanisms. - Building guardrails, compliance rules, and oversight workflows into the GenAI application platform. - Developing templates, guides, and sandbox environments for easy onboarding of new contributors and experimentation with new techniques. - Ensuring the development of user-facing applications in the GenAI application platform is easy and safe. - Contributing to and promoting good software engineering practices across the team. Your expertise should include: - A master's degree or Ph.D in Computer Science, Artificial Intelligence, Machine Learning, or a related field. - Proven experience in leading AI projects from conception to deployment in a consultancy or start-up environment. - Extensive knowledge of machine learning algorithms, data modeling, and simulation techniques. - Proficiency in Cloud technologies such as Azure, GCP, AWS. - Strong leadership skills with a proven track record of mentoring and developing talent. - Excellent communication skills, capable of conveying complex AI concepts to non-technical stakeholders. - Strategic thinking with a passion for problem-solving and innovation. - Subject matter expertise in statistics, analytics, big data, data science, machine learning, deep learning, cloud, mobile, and full stack technologies. - Hands-on experience in analyzing large amounts of data to derive actionable insights. - Working knowledge of traditional statistical model building, machine learning, deep learning, and NLP techniques. Please note that the company's additional details were not mentioned in the provided job description. As a Principal AI Engineer, you will play an instrumental and influential role in driving Generative AI vision, strategy, and architecture. Your key responsibilities include: - Architecting, building, maintaining, and improving new and existing suite of GenAI applications and their underlying systems. - Automating machine learning pipelines, monitoring performance and costs, and optimizing models using techniques such as LoRA/QLoRA. - Establishing reusable frameworks to streamline model building, deployment, and monitoring. - Incorporating comprehensive monitoring, logging, tracing, and alerting mechanisms. - Building guardrails, compliance rules, and oversight workflows into the GenAI application platform. - Developing templates, guides, and sandbox environments for easy onboarding of new contributors and experimentation with new techniques. - Ensuring the development of user-facing applications in the GenAI application platform is easy and safe. - Contributing to and promoting good software engineering practices across the team. Your expertise should include: - A master's degree or Ph.D in Computer Science, Artificial Intelligence, Machine Learning, or a related field. - Proven experience in leading AI projects from conception to deployment in a consultancy or start-up environment. - Extensive knowledge of machine learning algorithms, data modeling, and simulation techniques. - Proficiency in Cloud technologies such as Azure, GCP, AWS. - Strong leadership skills with a proven track record of mentoring and developing talent. - Excellent communication skills, capable of conveying complex AI concepts to non-technical stakeholders. - Strategic thinking with a passion for problem-solving and innovation. - Subject matter expertise in statistics, analytics, big data, data science, machine learning, deep learning, cloud, mobile, and full stack technologies. - Hands-on experience in analyzing large amounts of data to derive actionable insights. - Working knowledge of traditional statistical model building, machine learning, deep learning, and NLP techniques. Please note that the company's additional details were not mentioned in the provided job description.