Principal AI Engineer
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Working closely with business stakeholders and product teams, the Principal AI Engineer provides hands-on technical leadership and is accountable for the technical quality of assigned AI initiatives while promoting technical excellence, best practices, and innovation across the organization.
Responsibilities
Design, develop, and deploy AI and advanced analytics products that address high-value business challenges across the P&C business.
Drive the end-to-end implementation of machine learning, generative AI, and data science solutions from experimentation to production.
Collaborate with business, product, and technology stakeholders to translate requirements into robust and scalable solutions.
Apply and promote best practices in model development, software engineering, MLOps, testing, and AI product lifecycle management.
Provide technical guidance, mentoring, and peer reviews to Data Scientists and Machine Learning Engineers, helping raise engineering and modelling standards across the team.
Contribute technical expertise to complex initiatives and support innovation through experimentation with emerging AI technologies.
Ensure solutions meet performance, reliability, governance, and responsible AI standards.
Qualifications
Experience and Skills
- At least 8 years of experience designing, developing and deploying AI, machine learning and advanced analytics solutions in production environments.
- Strong expertise in machine learning, generative AI, statistical modelling and software engineering.
- Proven experience developing and maintaining production-grade AI products, applications and services.
- Advanced experience in model validation, performance evaluation, interpretability, and the identification and mitigation of model limitations and bias.
- Strong programming skills in Python, including object-oriented programming, API development and frameworks such as FastAPI.
- Experience building AI solutions on cloud platforms, preferably Microsoft Azure.
- Strong understanding of AI industrialization practices, including MLOps, model packaging, CI/CD, testing, deployment and monitoring.
- Experience with cloud-native technologies, including Kubernetes, PostgreSQL, and cloud storage services.
- Ability to solve complex technical problems and deliver robust, scalable and maintainable solutions from concept to production.
- Strong communication and collaboration skills and ability to work effectively in multidisciplinary teams.
- Experience with Databricks and Palantir Foundry is a strong plus.
Education
- Master’s degree in Science, Technology, Engineering, Mathematics, Computer Science, Actuarial Science or similar quantitative field
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