Director, Software Development - AI/ML Solutions and Delivery

Collaborate with business leaders, product managers, data scientists, and IT stakeholders to define requirements and manage expectations.,Mature MLOps system, embedding automated model training, versioning, and monitoring.,Tailor infrastructure and platform support to RBCI needs (integration and scalability).,Drive improved CI/CD pipeline for Airflow orchestration.,Implement AI model registry and versioning.,Lead operational AI support, including incident management, logging, log monitoring, AIOps, performance tuning, and model drift detection.,Define, communicate, and execute the AI/ML roadmap for insurance applications.,Stay updated on AI/ML advancements, especially in Agentic AI and process automation, evaluating their applicability to the insurance sector.,Champion ethical and responsible AI principles, ensuring compliance with regulatory requirements (e.g., OSFI, privacy).,Lead, mentor, and grow a team of Machine Learning Engineers, fostering innovation and collaboration.,Establish and enforce robust monitoring frameworks for deployed AI/ML productions, ensuring resilience, uptime, and disaster recovery.,Oversee talent acquisition, performance management, career development, and retention for the ML Engineering team.,Drive end-to-end development, deployment, and operation of robust, scalable, maintainable, and production-ready AI/ML solutions, integrated with existing systems.

8+ years of progressive experience in Machine Learning Engineering, AI development or Data Science, with 5+ years in a leadership role managing ML engineers or data scientists, with a strong emphasis on production system delivery and operations.,Proven experience working with or within the Insurance industry.,Exceptional leadership, mentorship, and team-building abilities.,Outstanding communication, presentation, and interpersonal skills.,Demonstrated experience in delivering production-grade AI/ML solutions from concept to production, and production management and support.,Experience with process automation initiatives and a strong understanding of Agentic AI paradigms.,Expertise in programming languages commonly used in ML (Python is essential, others like Java, Scala, R are a plus).,Proficiency with ML frameworks, libraries (e.g., PyTorch, TensorFlow, AirFlow, Hugging Face).,Strong understanding of MLOps principles, tools and platforms (e.g., MLflow, AWS SageMaker, Azure ML).,Solid experience with cloud platforms for ML infrastructure, modern data architecture, and scalable computing.,Demonstrated commitment to continuous learning and staying current with AI/ML advancements.,Knowledge of data engineering tools, such as Apache Beam, Apache Spark, or AWS Glue (Nice to Have).,Familiarity with agile development methodologies and version control systems (e.g., Git) (Nice to Have).,Certifications including AWS Certified Machine Learning – Specialty, Specialized MLOps Certifications, AI Engineering Professionals (Nice to Have).

Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Electrical Engineering or a related quantitative field; Master's or Ph.D. preferred.