The Director, Artificial Intelligence will lead an AI team supporting the Long Term Care (LTC) program in John Hancock and Manulife. This role is accountable for building and scaling AI solutions that improve business processes, generate strategic insights, and enable measurable impact across the organization. 

This is a leadership role that requires a strong combination of technical depth, business judgment, people leadership, and delivery excellence. The Director will manage a team of AI Engineers, Data Scientists, and technical professionals responsible for designing, developing, deploying, and maintaining production-grade AI and machine learning solutions. 

The ideal candidate is passionate about AI, a continuous learner, and someone who actively follows the latest trends in AI, GenAI, LLMs, machine learning, data science, software engineering, and cloud technologies. The candidate should have strong hands-on experience as an individual contributor in AI or data science and be able to guide teams through complex technical decisions. This includes experience across both traditional predictive analytics, machine learning, and MLOps as well as newer areas such as GenAI, LLMs, RAG, prompt orchestration, guardrails, LLMOps. 

The Director will work closely with business, technology, risk, compliance, operations, and executive stakeholders to identify opportunities, shape strategy, deliver high-quality AI solutions, and communicate impact in clear business terms. 

Position Responsibilities:   

• Lead the development and delivery of AI, ML, and GenAI solutions that support the Long Term Care business strategy in John Hancock and Manulife. 

• Manage and develop a team of AI Engineers, Data Scientists, and technical professionals, providing coaching, technical guidance, performance management, and career development. 

• Own complex AI initiatives from business problem definition through solution design, model development, engineering, deployment, adoption, and value measurement. 

• Partner with business leaders and stakeholders to identify high-value AI opportunities, translate business needs into technical solutions, and ensure AI products deliver measurable outcomes. 

• Provide technical leadership across traditional data science, predictive modeling, machine learning, GenAI, LLMs, and production AI engineering. 

• Guide the team in building modular, scalable, reusable AI and GenAI pipelines, including model development, feature engineering, model registries, automated retraining, and production deployment practices. 

• Oversee the deployment of production-grade AI services using modern cloud, MLOps, DevOps, and software engineering practices, including containerization, CI/CD, monitoring, observability, and model governance.  

• Ensure AI solutions are reliable, secure, scalable, cost-effective, well-documented, and aligned with enterprise architecture, security, risk, and compliance standards. 

• Lead the evaluation and adoption of GenAI patterns and tools, including LLMs, RAG, vector search, prompt engineering, guardrails, Azure OpenAI, Databricks, and internal AI platforms.  

• Drive strong engineering and model management practices, including testing, monitoring, model performance tracking, drift detection, challenger testing, and continuous improvement.  

• Communicate AI strategy, delivery progress, risks, business value, and model impact to senior leaders and cross-functional partners in a clear and practical way. 

• Build strong partnerships across business, technology, data, security, risk, compliance, and operations teams to support adoption and long-term success. 

• Stay current with advances in AI, GenAI, data science, engineering, cloud platforms, and emerging technology, and bring practical innovation into the team’s roadmap. 

• Support recruitment, onboarding, and development of strong AI, data science, and engineering talent. 

Required Qualifications:  

• 8+ years of experience in AI, data science, machine learning, analytics, software engineering, or related technical fields. 

• 3+ years of experience leading or managing technical teams, including Data Scientists, AI Engineers, Machine Learning Engineers, or similar roles. 

• Strong hands-on background as an individual contributor in AI, data science, machine learning, or AI engineering. 

• Proven experience delivering AI or ML solutions from concept to production, including business problem framing, model development, deployment, monitoring, and value measurement. 

• Strong experience with traditional data science and predictive analytics, including supervised learning, model validation, performance measurement, and business interpretation of model outputs. 

• Strong knowledge of GenAI and LLM-based solution patterns, including prompt engineering, RAG, embeddings, vector databases, guardrails, model evaluation, and responsible AI practices. 

• Advanced programming experience in Python and SQL. 

• Experience with cloud platforms, preferably Azure, and modern AI/ML platforms such as Azure ML, Azure OpenAI, Databricks, MLflow, or similar tools. 

• Strong understanding of MLOps, DevOps, CI/CD, testing, model monitoring, model governance, and production support. 

• Ability to work with engineering teams on scalable AI architecture, APIs, data pipelines, cloud-native services, and production deployment. 

• Strong communication skills, with the ability to explain complex AI concepts in simple business language for senior stakeholders. 

• Demonstrated ability to influence stakeholders and drive adoption of AI solutions across business and technology teams. 

• Advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field preferred; equivalent experience will also be considered. 

Preferred Qualifications:  

• Experience in insurance, financial services, healthcare, long-term care, claims, operations. 

• Experience leading AI products or platforms in a regulated enterprise environment. 

• Strong understanding of responsible AI, model risk management, data privacy, security, and compliance expectations. 

• Experience with large-scale data environments, including Databricks, Spark, Delta Lake, relational databases, and cloud data platforms. 

• Experience with production engineering practices such as Docker, Kubernetes, APIs, infrastructure-as-code, logging, tracing, monitoring, and alerting. 

• Strong consulting and stakeholder management skills, with the ability to operate effectively across business and technology teams. 

• Demonstrated ability to build high-performing, inclusive, and collaborative teams. 

• Active learner with a strong external network and a track record of staying current with AI, GenAI, engineering, and technology trends. 

Ideal Candidate Profile 

The ideal candidate is a hands-on AI leader who can operate at both the strategy level and the technical execution level. This person can engage senior business leaders on priorities and value, while also guiding AI Engineers and Data Scientists on architecture, modeling choices, engineering standards, and production delivery. 

This candidate should bring: 

• Deep curiosity and passion for AI and emerging technology. 

• Strong technical credibility with AI Engineers and Data Scientists. 

• Practical experience delivering AI solutions in production. 

• A strong understanding of both traditional ML and modern GenAI. 

• Clear communication and strong executive presence. 

• A collaborative leadership style focused on coaching, accountability, and measurable impact. 

When you join our team:  

• We’ll empower you to learn and grow the career you want.   

• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.   

• As part of our global team, we’ll support you in shaping the future you want to see.   

#LI-Hybrid