• 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 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. 

Preferred Qualifications:  

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, Mathematics, Statistics, Engineering, or a related technical field, or equivalent industry experience. 

• Master’s or PhD degree in a relevant discipline is an asset. 

• Experience in insurance, financial services, healthcare, Long-Term Care, claims, underwriting, risk management, or operations is an asset. 

• Experience with model governance, responsible AI, explainability, fairness testing, or regulated AI environments. 

• Experience with document intelligence, claims analytics, call center analytics, workflow automation, or knowledge management solutions. 

• Experience with vector databases or search technologies such as Azure AI Search, Elastic, Pinecone, FAISS, or similar tools. 

• Experience building production-grade GenAI applications using orchestration frameworks, agentic patterns, evaluation frameworks, and guardrails. 

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