Interesting read. Do you think AI will make senior engineers more valuable or reduce the gap between junior and senior devs?
Software Engineering After the AI Hype
Valentine Shi
●2 ●18 ●46
calendar_today ago
• schedule1 min read
— Originally published at valentineshi.dev
2 Comments
Austine
•
Valentine Shi
•
@[Austine] Thanks. Making remaining senior engineers more valuable? I believe so because of the higher engineering proficiency needed to validate the AI outputs.
The gap between those remaining seniors and junior would hardly diminish in terms of size, but time-wise, to grow from junior to senior (not in titles, in judgement capability) would probably require a little less time.
Please log in to add a comment.
🔥 Join developers growing publicly
Share your knowledge, build in public, and grow your developer presence with a global community.
Please log in to comment on this post.
More Posts
- © 2026 Coder Legion
- Feedback / Bug
- Privacy
- About Us
- Contacts
- Premium Subscription
- Terms of Service
- Refund
- Early Builders
chevron_left
15Posts
36Comments
12Connections
Senior Backend / Full-Stack / Founding Engineer specializing in complex business systems. Node.js, T... Show moreSenior Backend / Full-Stack / Founding Engineer specializing in complex business systems. Node.js, TypeScript, LLM Decision Workflows and AI-augmented accelerated product development.
I own - design and build production backend systems end-to-end in collaboration with product and engineering teams: from requirements, system architecture and contract-first APIs (OpenAPI) to ingestion pipelines, async orchestration, deployment, observability.
I actively use AI-augmented development workflows and spec-driven engineering to accelerate delivery while preserving the code validity and effectively minimizing defects. I design and implement AI/LLM programmatic decision workflows with constrained outputs, controlled vocabularies, and deterministic validation to ensure reliable behavior and eventual correctness in systems.
I ship high-reliability, low-firefight backend platforms for startups and early scale-ups, from day one built to be easily evolvable and fully prepared for continuous product change.
I use the following tools for that:
- Extended Model-Based Engineering (C4, UML/PlantUML for domain, architecture and fine sequence/state modeling)
- Domain-Driven Design (DDD) with Hexagonal Architecture
- Contract-First APIs (OpenAPI, AsyncAPI, JSON Schema validation, generated contracts enforcement)
- ATDD/TDD/E2E (Specification-by-Example, data providers, Testcontainers, integration-first backend testing)
- Event-driven and async workflow architectures (webhooks, queues, idempotence, state-based orchestration workflows)
- Deterministic automated code quality gates (linting, static analysis, git hook guards in CI, ~100% code coverage)
- Competent AI-augmented product engineering: OpenSpec SDD, agentic workflows, rapid prototyping, legacy refactoring, vibe-coding remediation, explicit engineering introduction
See my public engineering case: AI-Powered Image Generation & Publication System (Imagetron) at: https://valentineshi.dev/content/deliverables/K3aT7UX_RCC8ZO_fy9VinQ/ai-powered-image-generation-publication-system-imagetron
More details and other delivered public cases: https://valentineshi.dev Show less
I own - design and build production backend systems end-to-end in collaboration with product and engineering teams: from requirements, system architecture and contract-first APIs (OpenAPI) to ingestion pipelines, async orchestration, deployment, observability.
I actively use AI-augmented development workflows and spec-driven engineering to accelerate delivery while preserving the code validity and effectively minimizing defects. I design and implement AI/LLM programmatic decision workflows with constrained outputs, controlled vocabularies, and deterministic validation to ensure reliable behavior and eventual correctness in systems.
I ship high-reliability, low-firefight backend platforms for startups and early scale-ups, from day one built to be easily evolvable and fully prepared for continuous product change.
I use the following tools for that:
- Extended Model-Based Engineering (C4, UML/PlantUML for domain, architecture and fine sequence/state modeling)
- Domain-Driven Design (DDD) with Hexagonal Architecture
- Contract-First APIs (OpenAPI, AsyncAPI, JSON Schema validation, generated contracts enforcement)
- ATDD/TDD/E2E (Specification-by-Example, data providers, Testcontainers, integration-first backend testing)
- Event-driven and async workflow architectures (webhooks, queues, idempotence, state-based orchestration workflows)
- Deterministic automated code quality gates (linting, static analysis, git hook guards in CI, ~100% code coverage)
- Competent AI-augmented product engineering: OpenSpec SDD, agentic workflows, rapid prototyping, legacy refactoring, vibe-coding remediation, explicit engineering introduction
See my public engineering case: AI-Powered Image Generation & Publication System (Imagetron) at: https://valentineshi.dev/content/deliverables/K3aT7UX_RCC8ZO_fy9VinQ/ai-powered-image-generation-publication-system-imagetron
More details and other delivered public cases: https://valentineshi.dev Show less
More From Valentine Shi
Related Jobs
- Site Reliability Engineering ManagerMastercard · Full time · Mexico
- Technical Engineering ManagerStericycle · Full time · United Kingdom
- Software Engineering Intern - AI Enablement, Summer 2026Jack Henry & Associates · Full time · Springfield, MO
Commenters (This Week)
Joe Munene
6 comments
kube-gopher
2 comments
aminekhddz
1 comment
Contribute meaningful comments to climb the leaderboard and earn badges!