AI Engineer, Asset Management at Manulife in Boston, MA

Summary: Manulife is a leading international financial services provider, and they are seeking a motivated and dedicated AI Engineer focused on Investment Management Applications. In this key role, you will collaborate with investment teams to develop AI solutions, create data pipelines, and enhance existing tools and standards for MLOps practices. Responsibilities: - Work with front office analysts and portfolio managers to identify AI/ML project opportunities, develop lightweight prototypes, and implement key capabilities in a microservices framework - Lead data model design and implementation within the Asset Management AI group - Support automated data mapping, cataloging, and data quality testing prior to hand off to division data team for production - Monitor daily data quality checks, support internal teams on reconciliation and remediation as needed - Contribute to defining and implementing effective practices and standards for MLOps processes to ensure efficient and streamlined operations - Assist in implementing and managing CI/CD pipelines to automate testing and deployment processes, ensuring efficient and reliable operations - Stay updated with industry advancements in LLM and Generative AI models, enhancing MLOps capabilities to support LLM Ops needs - Collaborate with the information technology group to understand, align, and inform standards and governance to support data quality in CI/CD workflows in a DevSecOps framework - Support near term future state projects to improve ETL, compute, storage, and hosting in Azure using tools such as Databricks, Kubernetes, Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics (AWS, Google Cloud Equivalents ok as well) Required Qualifications: - Educational Background: 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 - AI Experience: 1-2 years of experience in building and maintaining AI infrastructure and tooling Preferred Qualifications: - Experience working with investment data, including understanding of security characteristics, propagation up and down the hierarchy, and the impact of corporation actions - Experience in 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 - Software Development: Proficiency in Python and experience with developing libraries and applications - CI/CD Tools: Experience with CI/CD tools and pipelines - Cloud and Containerization: Familiarity with cloud platforms (preferably Azure), ML development environments (Databricks), and containerization technologies (Docker, Kubernetes) - Experiment Tracking and Monitoring: Exposure to experiment tracking tools (e.g., MLFlow) and monitoring solutions - Strong communication skills, able to explain complex concepts to various collaborators - Understanding of ML Lifecycle: Basic understanding of the machine learning lifecycle and standard methodologies in MLOps - LLM/GenAI Models: Basic understanding of LLM/GenAI models and their operational requirements - Databricks Ecosystem Expertise: Hands-on expertise in the Databricks ecosystem, particularly model management using Unity Catalog - Full Stack Engineering Exposure: Ability to build proof of concepts/demos including both front-end and back-end development - Adaptability: Demonstrated proficiency in quickly picking up new frameworks and libraries Required Skills: Python, Data engineering, AI infrastructure, CI/CD pipelines Important Skills: Cloud platforms, Databricks, MLOps practices, ETL Development Nice-to-Have Skills: CFA Charter, ML Lifecycle, LLM/GenAI Models, Full Stack Engineering, communication, Adaptability Benefits: Health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans, Various retirement savings plans (including pension and a global share ownership plan with employer matching contributions), Financial education and counseling resources, Generous paid time off program in Canada includes holidays, vacation, personal, and sick days, Full range of statutory leaves of absence