Principal Software Engineer

This position is 100% remote anywhere in the US

Overview: We are seeking an experienced Principal Software Engineer with a strong background in Node to lead and contribute to the development of cutting-edge software solutions. The ideal candidate will possess extensive experience in software development, architecture, and team leadership. As a Principal Software Engineer, you will play a critical role in designing, developing, and maintaining high-performance SaaS applications, while also mentoring and guiding a team of engineers.

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:

• 8-10+ years of experience in software engineering, with a strong focus on building data-intensive and scalable SaaS applications
• Expert-level experience with Node.js for backend application development and architecture is required. Experience with Java is a plus but not required.
• Bachelor’s Degree (or higher) in technical field.
• Track record of Designing and developing of data products or software products.
• Expertise in Node for backend development; familiarity with frontend scripting languages such as react will be a plus. 
• Expertise with databases, particularly Mongo Atlas, and developing/consuming REST APIs. 
• Exposure to cloud-native technologies including Docker, Kubernetes, Kafka, and microservices architecture is a must. 
• Basic understanding of agile and scrum methodologies is a must.  
• Experience of AWS/Azure/GCP, Redis, Security Domain, Datadog, Code fresh, hands on will be advantageous  
• Working knowledge of data ETL will be preferred.
• Ability to communicate with various stakeholders at all levels of the organization.
• Excellent verbal and written communication skills.
• Excellent interpersonal skills and active listener.
• Able to set and meet time-sensitive goals.
• Able to handle multiple tasks simultaneously and adapt to change while providing structure to operations and go-to-market teams.

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

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