AI Engineer

Manulife

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

Posted 9/29/2026full-timeBoston • Massachusetts • United StatesMid-LevelSenior💰 $90,160 - $167,440 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI and ML solution design, deployment, and operationalization, with a strong focus on MLOps practices and cloud-native infrastructure. Proficient in collaborating with cross-functional teams to deliver scalable AI products while ensuring compliance and governance.

Highest-signal resume keywords

AI EngineeringMLOps ExperiencePython ProgrammingCloud Platforms (Azure)GenAI and LLM Solutions

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPredictive AnalyticsFeature EngineeringModel EvaluationData ProcessingSQLETL/ELTContainerizationAPI DesignAutomated Testing

Soft Skills

Cross-Functional CollaborationClear CommunicationProblem-SolvingMentoring

Tools & Technologies

Azure MLDatabricksMLflowDockerKubernetesGitHub ActionsAzure OpenAIVector DatabasesElasticPinecone

Industry Keywords

Financial ServicesLong-Term CareInsuranceRisk ManagementModel GovernanceResponsible AIDocument IntelligenceClaims AnalyticsWorkflow AutomationKnowledge Management

Tech Stack

Tools & technologies

AzureCloudDockerETLKubernetesPythonSparkSQL

About the role

Key responsibilities & impact

Design, build, and deploy production-ready AI and ML solutions supporting 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 reusable ML and GenAI pipelines covering 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 RAG, prompt orchestration, document intelligence, summarization, classification, and workflow automation
Deploy AI services using containerization, CI/CD, automated testing, version control, and cloud-native infrastructure
Monitor models for performance, drift, bias, accuracy degradation, latency, cost, and reliability using MLOps and LLMOps practices
Build observability capabilities with logging, tracing, metrics, alerts, dashboards, and service-level monitoring
Collaborate with Risk, Legal, Compliance, Security, Architecture, and Cloud teams to ensure secure, compliant, explainable solutions
Support model governance, documentation, validation, auditability, model lineage, and responsible AI controls
Evaluate and adopt tools and platforms across Azure, Databricks, Azure OpenAI, MLflow, vector databases, and internal AI platforms
Integrate AI services with business workflows through APIs, event-driven architecture, batch pipelines, and enterprise applications
Improve solution quality, scalability, maintainability, and cost efficiency
Stay current with AI, ML, GenAI, LLMOps, software engineering, cloud platforms, and financial services technology trends
Mentor junior engineers and data scientists on production engineering, clean code, testing, monitoring, and MLOps/LLMOps best practices

Requirements

What you’ll need

3+ years of experience in AI Engineering, ML Engineering, Software Engineering, Data Science Engineering, or a related technical role
Strong programming skills in Python
Experience deploying ML or AI models into production cloud environments
Hands-on MLOps experience, including model versioning, model registry, CI/CD, automated testing, monitoring, retraining workflows, and production support
Experience monitoring production model performance, including accuracy, drift, latency, stability, reliability, and business performance indicators
Understanding of traditional machine learning and predictive analytics, 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 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 with structured and unstructured data
Experience with ETL/ELT, Spark, Databricks, Delta Lake, data quality, and scalable data pipelines
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 technical and non-technical stakeholders
Ability to balance speed, quality, risk, and long-term maintainability
Bachelor’s degree in a relevant technical field or equivalent industry experience is preferred
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 is preferred
Experience with document intelligence, claims analytics, call center analytics, workflow automation, or knowledge management solutions is preferred
Experience with vector databases or search technologies such as Azure AI Search, Elastic, Pinecone, FAISS, or similar tools is preferred
Experience building production-grade GenAI applications using orchestration frameworks, agentic patterns, evaluation frameworks, and guardrails is preferred

Benefits

Comp & perks

Incentive programs and incentive compensation tied to business and individual performance
Health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage
Adoption/surrogacy and wellness benefits
Employee/family assistance plans
Retirement savings plans, including pension/401(k) savings plans
Global share ownership plan with employer matching contributions
Financial education and counseling resources
Up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time annually in the U.S.
Statutory leaves of absence
Reasonable accommodation during the application process