Collaborating with front office analysts and portfolio managers to identify AI/ML project opportunities
Developing lightweight prototypes
Implementing key capabilities in a microservices framework
Leading data model design and implementation within the Asset Management AI group
Supporting automated data mapping, cataloging, and data quality testing
Monitoring daily data quality checks
Contributing to defining and implementing effective practices and standards for MLOps processes
Assisting in implementing and managing CI/CD pipelines
Staying updated with industry advancements in LLM and Generative AI models
Collaborating with the information technology group to support data quality in CI/CD workflows

Requirements

PhD, Masters or equivalent experience in Computer Science, Data Science, Statistics, or a related field
CFA Charter or demonstrated progress
1-2 years of experience in building and maintaining AI infrastructure and tooling
Experience working with investment data
Experience in deploying and maintaining RAG pipelines
Working with vector databases like MongodB/ LancedB
Proven success with ETL Development, schema management, API design, data operations, and data architecture
Proficiency in Python and experience with developing libraries and applications
Experience with CI/CD tools and pipelines
Familiarity with cloud platforms (preferably Azure)
Exposure to experiment tracking tools (e.g., MLFlow)
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

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AIMLmicroservicesdata modelingdata quality testingMLOpsCI/CDPythonETL developmentdata architecture

Soft Skills

collaborationleadershipcommunication

Certifications

PhDMastersCFA Charter