Senior AI Engineer

Manulife•Boston, MA

17d•Hybrid

About The Position

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.

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.

Nice To Haves

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.

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.