Hybrid AI Engineer – Asset Management

at Manulife

• Job title: AI Engineer – Asset Management
• Job type: Full Time
• Experience level: Junior
• Salary: $127,330 - $236,470 per year
• Degree requirement: Postgraduate Degree
• Tech skills: Azure, Cloud, Docker, ETL, Kubernetes, MongoDB, Python, Unity
• Location requirements: Hybrid, Boston, United States

About the role

• AI Engineer focusing on Investment Management Applications, collaborating with investment teams and developing AI solutions. Involves data engineering, tooling, and standards development.

Responsibilities

• Collaborating with front office investment teams to develop AI solutions
• Sourcing investment market and portfolio data
• Developing data pipelines, prototype development of AI applications
• Maintaining and enhancing Python libraries and lightweight applications
• Supporting architecture and scale efforts
• Contributing to the development of tools and standards to operationalize LLM and Generative AI use-cases

Requirements

• PhD, Masters or equivalent experience in Computer Science, Data Science, Statistics, or a related field
• CFA Charter or demonstrated progress
• Data engineering experience in an investment context
• 1-2 years of experience in building and maintaining AI infrastructure and tooling
• Experience working with investment data
• Experience deploying and maintaining RAG pipelines and working with vector databases like MongodB/ LancedB
• Experience developing within an agentic framework, using Langgraph, Semantic Kernel, etc.
• Proven success with ETL Development, schema management, API design, data operations, and data architecture
• Understanding of the research process showcased by at least one publication
• Proficiency in Python and experience with developing libraries and applications
• Experience with CI/CD tools and pipelines
• Familiarity with cloud platforms (preferably Azure), ML development environments (Databricks), and containerization technologies (Docker, Kubernetes)
• Exposure to experiment tracking tools (e.g., MLFlow) and monitoring solutions
• Basic understanding of the machine learning lifecycle and standard methodologies in MLOps
• Basic understanding of LLM/GenAI models and their operational requirements
• Hands-on expertise in the Databricks ecosystem, particularly model management using Unity Catalog
• Ability to build proof of concepts/demos including both front-end and back-end development
• Demonstrated proficiency in quickly picking up new frameworks and libraries

Benefits

• health, dental, mental health, vision insurance
• 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
• generous paid time off including up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time