Senior AI Engineer

The Sr. AI Engineer will join the AI team supporting the Long-Term Care program in John Hancock and Manulife. This role will help design, build, deploy, and scale production-grade AI solutions that improve business outcomes, operational efficiency, risk management, and customer experience across the Long-Term Care value chain. 

The ideal candidate is passionate about AI and technology, a lifelong learner, and someone who actively follows the latest trends in AI Engineering, ML Engineering, Generative AI, LLMs, cloud-native development, and modern software engineering. This individual should bring strong hands-on experience in deploying models to production, monitoring model performance, and applying established MLOps and LLMOps frameworks. 

This role requires a strong blend of traditional data science, predictive analytics, machine learning, GenAI, and production engineering. The successful candidate will work closely with Data Scientists, Data Engineers, Product Owners, Business Partners, and Technology teams to turn prototypes into reliable, scalable, and well-governed AI products. 

Position Responsibilities:   

• Design, build, and deploy production-ready AI and ML solutions that support the Long-Term Care program across John Hancock and Manulife. 

• Partner with Data Scientists, Data Engineers, Business Analysts, and Product teams to translate business needs into scalable AI products. 

• Build and maintain modular, reusable ML and GenAI pipelines, including data processing, feature engineering, model training, evaluation, deployment, and monitoring. 

• Operationalize traditional ML models and predictive analytics solutions, including classification, regression, forecasting, risk scoring, segmentation, and anomaly detection. 

• Implement GenAI and LLM-based solutions, including retrieval-augmented generation, prompt orchestration, document intelligence, summarization, classification, and intelligent workflow automation. 

• Deploy models and AI services into production using modern engineering practices such as containerization, CI/CD, automated testing, version control, and cloud-native infrastructure. 

• Monitor production models for performance, data drift, model drift, bias, accuracy degradation, latency, cost, and reliability using established MLOps and LLMOps practices. 

• Build observability capabilities, including logging, tracing, metrics, alerts, dashboards, and service-level monitoring. 

• Collaborate with Risk, Legal, Compliance, Security, Architecture, and Cloud teams to ensure AI solutions are secure, compliant, explainable, and aligned with enterprise standards. 

• Support model governance activities, including documentation, validation, auditability, model lineage, and responsible AI controls. 

• Evaluate and adopt fit-for-purpose tools, frameworks, and platforms across Azure, Databricks, Azure OpenAI, MLflow, vector databases, and internal AI platforms. 

• Engineer AI services that integrate with business workflows through APIs, event-driven architecture, batch pipelines, and enterprise applications. 

• Continuously improve solution quality, scalability, maintainability, and cost efficiency. 

• Stay current with emerging trends in AI, ML, GenAI, LLMOps, software engineering, cloud platforms, and financial services technology, and share relevant learnings with the team. 

• Mentor junior engineers and data scientists on production engineering standards, clean code, testing, monitoring, and MLOps/LLMOps best practices. 

Required Qualifications:  

• 5+ years of experience in AI Engineering, ML