Principal Backend Engineer (Go/PostgreSQL)

Accountabilities

Backend Engineering and Product Development: Design, develop, and maintain customer-facing features within a multi-tenant backend architecture using Go and PostgreSQL, ensuring solutions are reliable, maintainable, and scalable.
Customer Data Integration: Connect and consolidate information from multiple sources, including CRM platforms, customer conversations, and support systems, to create a consistent and actionable view of customer accounts.
AI Workflow Engineering: Build production-grade AI workflows that analyze information, generate customer intelligence, and execute permission-based actions while maintaining traceable and auditable records.
LLM and Application Architecture: Determine which tasks should be handled by large language models and which require deterministic application logic, calculations, or persistent data storage to ensure accuracy and control.
Database Design and Optimization: Design efficient PostgreSQL schemas, troubleshoot complex queries, resolve performance bottlenecks, and optimize data access patterns to support growing workloads.
Reliability and Quality Assurance: Develop workflows that can be safely rerun, implement appropriate error-handling mechanisms, and monitor output quality to identify regressions and maintain consistent results.
Scalability and Performance: Ensure services remain responsive and dependable across customer workspaces ranging from approximately 100 to 100,000 accounts.
Infrastructure and Deployment: Support production operations across AWS and Docker-based environments, including enterprise installations and on-premises deployments.
End-to-End Ownership: Take responsibility for features throughout their lifecycle, from understanding customer problems and designing technical solutions to implementation, deployment, monitoring, and incident resolution.
Customer-Driven Improvement: Work closely with users and incorporate customer feedback to refine existing functionality, improve usability, and deliver measurable product value.
Rapid Delivery and Iteration: During the first 30 days, become familiar with the existing system by delivering approximately two to three fixes per week and one to two improvements to existing features.
Technical Leadership: Within three to six months, take ownership of a major product capability from initial requirements through production adoption. Potential areas include customer intelligence pipelines, customer success management agents, renewal automation, and expansion signals.
AI-Assisted Development: Use AI coding tools effectively to accelerate implementation while maintaining high standards for code quality, readability, testing, and long-term maintainability.
Requirements
Professional Experience: At least eight years of backend engineering experience in product-based organizations, including meaningful experience working in startup environments. Candidates must currently be working at a product-based organization.
Go Development: Strong hands-on experience designing, building, and operating production services using Go.
PostgreSQL Expertise: Proven ability to design relational database schemas, debug and optimize queries, and address database performance and scalability challenges.
Production AI Experience: Experience shipping customer-facing products that use large language models to make judgments or support decisions, beyond prototypes, demonstrations, or experimental projects.
End-to-End Technical Ownership: Demonstrated experience owning services or features from initial design through production deployment, including ongoing reliability, monitoring, and issue resolution.
Problem-Solving Skills: Ability to navigate ambiguous requirements, independently identify practical solutions, and translate complex customer problems into working product capabilities.
AI Development Tools: Regular experience using AI-powered development tools to build products, improve productivity, and produce clean, maintainable code.
Product Engineering Mindset: Experience working closely with customers or end users and using their feedback to guide product improvements and technical priorities.
System Design and Architecture: Strong understanding of backend architecture, distributed systems, multi-tenant applications, data integration, and scalable service design.
Reliability and Performance: Ability to design robust systems with appropriate error handling, safe retries, repeatable workflows, and mechanisms for monitoring application behavior and AI output quality.
Infrastructure Knowledge: Familiarity with Docker, AWS, production deployments, and supporting applications across enterprise and on-premises environments.
Communication and Collaboration: Strong communication skills, sound technical judgment, and the ability to explain architectural decisions and trade-offs clearly while working independently.
Additional Advantages: A public GitHub profile, relevant side projects, or a technical blog demonstrating engineering expertise and personal initiative would be a strong plus.
Benefits
Competitive Compensation: Annual compensation of approximately ₹50,00,000 to ₹1,00,00,000, as advertised for the role.
Remote Work: Full-time remote opportunity for candidates based in India.
Contractor Engagement: Full-time contractor arrangement with a US-based organization. The advertised terms state that no deductions will be made from the agreed compensation by the hiring company.
High Ownership: Direct responsibility for important product capabilities, with the opportunity to influence architecture, engineering practices, and product direction.
AI-First Engineering Environment: Work extensively with large language models, AI workflows, and AI-assisted coding tools to solve practical customer problems.
Meaningful Product Impact: Build customer intelligence systems that help revenue teams and AI agents understand accounts, automate workflows, and make better-informed decisions.
Technical Autonomy: Have a meaningful voice in architectural decisions, implementation strategies, and the evolution of the backend platform.
Fast Learning and Development: Work on challenging problems involving data integration, scalable systems, AI reliability, and production-grade software engineering.
Direct Customer Impact: Improve a real product through close engagement with users and rapid feedback-driven development.
Interview Process: Expect three to four technical interview rounds covering programming, system design, and AI tool usage, along with a take-home assignment.