Verified Job
Engineering - Architecture
Software Engineer (Backend + AI Native)
Engineering - Architecture
#1816
Remote / WFH
Hoichoi
Job Description
Job description
We are looking for engineers (backend heavy + AI Native) to join our tech team based in Kolkata. This role sits at the core of revenue infrastructure — covering payment orchestration, subscription lifecycle, and high-scale backend systems.
You won't maintain systems. You'll design, ship, and scale them. Every deployment impacts millions of users and real revenue.
What You'll Own + Key Responsibilities :
• Own end-to-end technical decisions — API contracts, data models, service boundaries, and integration patterns — across a live, high-traffic streaming platform.
• Think architecturally first: identify the right abstraction, lead the trade-off conversation, and document decisions that the broader team can reason about and build on.
• Use AI-native development tools (Cursor, Claude Code, GitHub Copilot) as your default workflow — not as shortcuts, but as force multipliers where you own the output and know when to override.
• Lead technical design reviews for new capabilities, surfacing risks, edge cases, and cross-system dependencies before a single line gets committed.
• Diagnose and resolve production issues across backend systems — trace root causes, write clean fixes, and prevent recurrence through deliberate design choices.
• Partner closely with product, growth, content, and data teams to translate fast-moving business requirements into robust, maintainable backend solutions.
• Contribute to hoichoi Cortex — our central data aggregation and intelligence layer — by designing clean API contracts and data flows that connect content signals, user behaviour, and business metrics.
• Evaluate and integrate third-party services, LLM APIs, and external tools — making deliberate build-vs-buy-vs-compose decisions with clear justifications.
• Review code that a future-you would be proud to maintain: readable, testable, and defensible.
If you have :
• 1–5+ years of backend engineering experience with production ownership — at least one system that real users depend on and you were responsible for.
• Strong fundamentals in at least one backend language and comfort reasoning about system design, data modelling, and API contracts.
• Ability to read a system, understand its constraints, and make architectural recommendations — not just execute tickets handed down to you.
• Experience working in a fast-moving product team where requirements change mid-sprint and priorities shift without warning.
• Default user of AI coding tools (Cursor, Copilot, Claude Code, etc.) on real shipped features — with at least one concrete example where AI generated significant code that you evaluated, debugged, or chose to override.
• Comfort with ambiguity: you can move without complete information and know when to decide independently versus when to ask.
And (or) also have :
• Familiarity with streaming or media tech: CDNs, adaptive bitrate, DRM, or video processing pipelines.
• Experience with event-driven architectures, message queues (Kafka, SQS, RabbitMQ), or distributed microservices.
• Prior work on data pipelines, analytics integrations, or internal developer tooling.
• Exposure to LLM APIs, prompt engineering, or AI-augmented product features shipped to real users.
Candidates must have an active GitHub or portfolio for skill evaluation.
We are looking for engineers (backend heavy + AI Native) to join our tech team based in Kolkata. This role sits at the core of revenue infrastructure — covering payment orchestration, subscription lifecycle, and high-scale backend systems.
You won't maintain systems. You'll design, ship, and scale them. Every deployment impacts millions of users and real revenue.
What You'll Own + Key Responsibilities :
• Own end-to-end technical decisions — API contracts, data models, service boundaries, and integration patterns — across a live, high-traffic streaming platform.
• Think architecturally first: identify the right abstraction, lead the trade-off conversation, and document decisions that the broader team can reason about and build on.
• Use AI-native development tools (Cursor, Claude Code, GitHub Copilot) as your default workflow — not as shortcuts, but as force multipliers where you own the output and know when to override.
• Lead technical design reviews for new capabilities, surfacing risks, edge cases, and cross-system dependencies before a single line gets committed.
• Diagnose and resolve production issues across backend systems — trace root causes, write clean fixes, and prevent recurrence through deliberate design choices.
• Partner closely with product, growth, content, and data teams to translate fast-moving business requirements into robust, maintainable backend solutions.
• Contribute to hoichoi Cortex — our central data aggregation and intelligence layer — by designing clean API contracts and data flows that connect content signals, user behaviour, and business metrics.
• Evaluate and integrate third-party services, LLM APIs, and external tools — making deliberate build-vs-buy-vs-compose decisions with clear justifications.
• Review code that a future-you would be proud to maintain: readable, testable, and defensible.
If you have :
• 1–5+ years of backend engineering experience with production ownership — at least one system that real users depend on and you were responsible for.
• Strong fundamentals in at least one backend language and comfort reasoning about system design, data modelling, and API contracts.
• Ability to read a system, understand its constraints, and make architectural recommendations — not just execute tickets handed down to you.
• Experience working in a fast-moving product team where requirements change mid-sprint and priorities shift without warning.
• Default user of AI coding tools (Cursor, Copilot, Claude Code, etc.) on real shipped features — with at least one concrete example where AI generated significant code that you evaluated, debugged, or chose to override.
• Comfort with ambiguity: you can move without complete information and know when to decide independently versus when to ask.
And (or) also have :
• Familiarity with streaming or media tech: CDNs, adaptive bitrate, DRM, or video processing pipelines.
• Experience with event-driven architectures, message queues (Kafka, SQS, RabbitMQ), or distributed microservices.
• Prior work on data pipelines, analytics integrations, or internal developer tooling.
• Exposure to LLM APIs, prompt engineering, or AI-augmented product features shipped to real users.
Candidates must have an active GitHub or portfolio for skill evaluation.
