Senior AI Engineer

About the role

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.

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, Product Owners, 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.

Requirements

• 5+ years of experience in AI Engineering, ML Engineering, Software Engineering, Data Science Engineering, or a related technical role.
• Strong programming skills in Python, with experience building reliable, maintainable, and production-quality code.
• Proven experience deploying ML or AI models into production cloud environments.
• Hands-on experience with MLOps practices, including model versioning, model registry, CI/CD, automated testing, monitoring, retraining workflows, and production support.
• Experience monitoring model performance in production, including accuracy, drift, latency, stability, reliability, and business performance indicators.
• Strong understanding of traditional machine learning and predictive analytics techniques, including supervised learning, unsupervised learning, feature engineering, model evaluation, and experimentation.
• Practical experience with GenAI and LLM-based solutions, including prompt engineering, RAG, embeddings, vector search, evaluation, and guardrails.
• Experience working with cloud platforms, preferably Azure, and tools such as Azure ML, Azure OpenAI, Databricks, MLflow, Docker, Kubernetes/AKS, GitHub Actions, or Azure DevOps.
• Strong SQL skills and experience working with structured and unstructured data.
• Experience with data engineering concepts, including ETL/ELT, Spark, Databricks, Delta Lake, data quality, and scalable data pipelines.
• Strong understanding of software engineering best practices, including API design, unit testing, integration testing, code reviews, documentation, and secure development.
• Ability to work with cross-functional teams and communicate technical concepts clearly to both technical and non-technical stakeholders.
• Demonstrated ability to balance speed, quality, risk, and long-term maintainability.