Principal Software Engineer

Overview

What you will do

• Design and architect scalable, robust, and high-performance Node-based applications.
• Ensure software solutions align with business objectives and meet performance, scalability, and reliability requirements.
• Leads architecture for all components in an investment segment. May focus on one component but must have expertise across all components.
• Responsible for design of systems in the investment segment.
• Actively supports Product Manager in the Product Planning, business performance of one or more products.
• Understand requirements that comes from stakeholders and helps turn them into consumable backlog items for the team.
• Write clear, compelling, and detailed (technical) user epics and stories with user acceptance criteria. Participate in story grooming exercises for crisp and unambiguous documentation and communication of features to be developed.
• Coordinate with appropriate architects to ensure tech decisions are made and implemented. Also Identifying business process improvements and opportunities for automation.

What we are looking for

AI Skills/Knowledge

• Proficiency with AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Amazon CodeWhisperer) to accelerate development velocity
• Experience using AI-powered code review agents to improve code quality and reduce review cycles
• Ability to critically evaluate and validate AI-generated code for correctness, security, and maintainability
• Familiarity with LLM APIs or agentic workflows
• Comfortable incorporating generative AI tools into daily engineering workflows (debugging, documentation, test generation)

Preferred Skills

• Experience on git hub spec driven development
• Understands concept of constitutions and skills and agents MD
• Hands-on experience building or architecting agentic workflows and multi-step AI pipelines
• Experience with prompt engineering and fine-tuning strategies for developer productivity use cases
• Familiarity with AI observability and evaluation frameworks (e.g., LangSmith, Weights & Biases)
• Exposure to Retrieval-Augmented Generation (RAG) patterns and vector database integrations
• Experience leading or mentoring teams in adopting AI tooling and best practices
• Knowledge of responsible AI principles — bias detection, model safety, and governance at the enterprise
• #LI-ZB1

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