Full Stack Engineer, Portfolio Management Group, Associate

About the Role

The Full Stack Engineer builds end-to-end, AI-enabled research applications that deliver data, models, and insights seamlessly to investors and researchers. Embedded within a PMG research pod, this role works closely with the AI Lead, Solutions Lead, and Research Engineers to translate investor workflows into intuitive, interactive application experiences.

Full Stack Engineers combine front-end and back-end development skills with an understanding of research needs to build applications that fit naturally into how investors conduct research. They integrate data, analytics, and AI capabilities into user interfaces that enhance investment decision-making.

This role uses modern application frameworks across the stack (such as Python APIs, AI/LLM services, cloud data platforms, and contemporary web UI frameworks), without requiring deep specialization in any single technology.

Key Responsibilities

Research Application Development (End-to-End)

• Build full-stack research applications that combine data, analytics, and AI features.

• Develop back-end services/APIs to deliver research datasets, model outputs, and interactive capabilities.

• Implement front-end interfaces that present insights clearly and support investor workflows.

Collaboration with AI Leads & Research Teams

• Work closely with the AI Lead to understand investor research use cases, workflows, and decision processes.

• Embed business context into applicationdesignso tools feel intuitive, relevant, and tailored to investor needs.

• Collaborate with the Solutions Lead to refine workflows, user journeys, and feature priorities.

AI Integration

• Integrate AI capabilities (e.g., natural language components, retrieval flows, model-driven insights) into research applications.

• Implement back-end logic to support AI features, in partnership with the AI Lead.

Data Access & Research Workflow Integration

• Build application services that interact with cloud data platforms and internal research pipelines.

• Ensure applications connect cleanly to research datasets prepared by Research Engineers.

• Optimizedata access patterns for performance and reliability.

Quality, Reliability & Deployment

• Collaborate with horizontal Ops Intelligence teams to ensureappropriate instrumentation, monitoring, and operational reliability.

• Apply good engineering practices including testing, version control, structured deployment, and maintainability.

• Support continuous improvement and iteration based on user feedback.

Pod & Engineering Hub Collaboration

• Work as a core member of thepod’sengineering group (AI Lead, Solutions Lead, Research Engineers).

• Coordinate with horizontal engineering groups (Workflow Automation, Ops Intelligence) toleverageplatform capabilities and shared tooling.

• Contribute to reusable components, patterns, and best practices across pods.

Qualifications

Required:

• 3+ years of experience building production-grade full-stack applications using a modern Python backend framework (e.g.,FastAPI, Flask,etc) and a modern frontend framework (e.g., React/Next.js) with TypeScript.

• Proven experience designing, consuming, and securing REST /GraphQLAPIs that integrate with cloud data platforms (e.g., Snowflake,BigQuery,etc).

• Experience collaborating closely with data engineers on complex, high-volume data pipelines, ensuring backend services, APIs, and data contracts integrate cleanly with their workflows.

• Hands-on experience integrating analytics or AI/LLM features into applications (prompting, retrieval/RAG, orchestration, monitoring).

• Experience deploying andoperatingfull-stack applications on major cloud platforms (GCP,AWSorAzure), including CI/CD, observability, and performance tuning.

• Strong ability to translatebusiness ordomain workflows into technical designs, data contracts, and scalable application architectures.

• Excellent communication skills with non-technical stakeholders; able to gather requirements, demo iteratively, and document systems clearly.

Preferred:

• Priorworkon research, analytics, or portfolio / risk / market data–driven applications in theinvestment management or broader financial services domain.

• Exposure tobuildingmultimodal AI applications.

Key Attributes

• Strong user-centric mindset—learnshow investors work anddesignsapplications that support their thinking.

• Balanced engineering skills across both front-end and back-end development.

• Collaborative and comfortable working in cross-functional pod structures.

• Curious, adaptable, and eager to experiment withnew technologiesand capabilities.

• Strong ownership and commitment to building reliable, maintainable systems.