Senior AI Engineer

• 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 andmaintainmodular 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:

• Bachelors degree in Computer Science Software Engineering Data Science Mathematics Statistics Engineering or a related technical field or equivalent industry experience.

• Masters 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:

• Wellempower you to learn and grow the career you want.

• Wellrecognize and support you in a flexible environment where well-being and inclusion are more than just words.

• As part of our global teamwellsupport you in shaping the future you want to see.

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