AI Engineer

Qualifications

Must have: 2 - 5 years’ experience in AI Engineering and/or Machine Learning (ML) with a focus on LLMs, with deep expertise in writing, and reviewing production code in Python Understanding the development lifecycle for LLMs— developing data sets for pre-training, instruction tuning, and preference alignment alongside the modelling techniques for each stage and LLM deployment is as MAJOR plus Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale Experience building and deploying solutions on cloud platforms (AWS, Azure, or GCP) Experience on distributed, high throughput and low latency architectures Strong fundamentals in NLP techniques for text representation, semantic extraction techniques, data structures and modeling Experience building software on top of major container technology (Kubernetes, Docker etc.) Knowledge of version control using Jenkins, GitHub Actions, GitLab CI, Jenkins, or Azure DevOps Solid understanding of data engineering concepts and working with large-scale datasets Experience implementing ML Ops practices and production-grade AI systems Familiarity with data privacy, model governance, and responsible AI principles

Nice to have: Experience and Knowledge of Finance Domain: Understanding of finance concepts, workflows, or platforms is a strong asset for this role along with Knowledge of financial processes such as close, consolidation, reporting, and audit Exposure to regulatory and compliance frameworks (e.g., SOX, GDPR, model risk management) Experience with ERP systems (e.g., SAP, Oracle) and finance data ecosystems Experience defining system architecture and exploring technical feasibility tradeoffs is a plus Strong understanding of AI risk, explainability, and auditability Familiarity with end-to-end application development using full stack is a plus Experience in P&C insurance is a plus

Key Competencies: Strong problem-solving and analytical thinking Ability to work across cross-functional and global teams Excellent communication and stakeholder management skills High attention to governance, risk, and compliance considerations Ability to balance innovation with control and scalability

What Success Looks Like: Delivery of scalable AI capabilities embedded within finance processes Measurable improvements in key KPIS - efficiency, accuracy, and compliance Strong adoption of AI solutions across finance teams globally Robust governance and audit-ready AI implementations

Chubb Canada does not use artificial intelligence (AI) tools to assess, screen, or select applicants. At Chubb we are committed to providing equal employment opportunities to all employees and applicants. It is our policy to provide equal employment opportunities to employees and applicants based on job-related qualifications and ability to perform a job. If you require accommodation during the hiring process or upon hire, please inform Human Resources. If a selected applicant requests accommodation during the recruitment process, Chubb will consult with the applicant in order to provide suitable accommodation that takes into account the applicant’s accessibility needs.