Staff AI Engineer

NXT Door Retail; Chennai District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

NXT Door Retail is seeking a senior software engineer to lead end-to-end AI-powered product delivery from problem framing to production operations. You will design and ship full-stack systems, including frontend, backend APIs, data layers, and AI/ML services, not limited to models or prompts.

Ideal candidates have hands-on experience shipping LLM/GenAI in production (RAG, agents, evaluation) and can operate across React/Next, Node/Python/Go, databases, queues, cloud, and CI/CD, taking ambiguous

Qualifications

• Must have production-level coding mindset and ownership.
• Proven full-stack design and implementation across frontend, backend, APIs, data stores, and cloud infra.
• Experience shipping LLM/GenAI or ML systems in production (RAG, agents, evaluation).
• Familiar with AI-assisted development tools as part of daily work.
• Ability to choose the right tech stack for a problem.

Responsibilities

• Own end-to-end delivery of AI-powered products from framing to production.
• Design, build, and ship full-stack systems including UI, APIs, data layers, and AI services.
• Turn ideas into shipped features with API design and data integration.
• Productionize LLM/GenAI features and integrate into user apps.
• Design scalable, observable, cost-aware architectures.
• Own data pipelines, embeddings, and evaluation loops in production.
• Work across stack: React/Next, Node/Python/Go, databases, queues, cloud, CI/CD.
• Collaborate with product/design/engineering to clarify ambiguous problems.
• Review code and mentor engineers on AI and full-stack practices.
• Set engineering standards for AI quality, security, and release confidence.
• Debug production issues end-to-end.

Skills

Full-stack depth

AI-assisted development tools

GenAI in production

CI/CD

Distributed systems

End-to-end ownership

Education

Degree in Computer Science / Engineering

Tools

Cursor

GitHub Copilot

Job description

Responsibilities

• Own end-to-end delivery of AI-powered products from problem framing and architecture through implementation, deployment, and production operations
• Design, build, and ship full-stack systems: frontend, backend APIs, data layers, and AI/ML services not just models or prompts
• Take a product from idea to production independently: design APIs, build UIs, wire data flows, integrate models, and operate the system
• Build and productionize LLM / GenAI features (RAG, agents, tool-calling, evaluation, guardrails) and integrate them into real user-facing applications
• Design scalable application and model-serving architectures that are reliable, observable, and cost-aware
• Own data pipelines, retrieval, embeddings, and evaluation loops so AI features stay accurate and measurable in production
• Work across the stack as needed React/Next (or equivalent), Node/Python/Go services, databases, queues, cloud, and CI/CD without waiting on a specialist for every layer
• Partner with product, design, and engineering to turn ambiguous problems into shipped features
• Review code, raise the bar on system design, and mentor engineers on both AI and full stack practices
• Establish engineering standards for AI quality, evaluation, security, and release confidence
• Debug production issues across the full path: UI, API, infra, data, and model behavior

Requirements

• 15+ years of software engineering, with recent hands-on ownership of production systems.
• Proven full-stack depth: can design and implement frontend, backend, APIs, data stores, and cloud infrastructure not limited to AI-only work.
• Hands‑on with AI‑assisted development tools (Cursor, GitHub Copilot, and similar) as part of day‑to‑day engineering.
• Hands‑on experience shipping LLM / GenAI or ML systems in production (RAG, agents, fine‑tuning, evaluation, or equivalent).
• Comfortable choosing and using the right stack for the problem (e.g. TypeScript/Python, React or similar, REST/GraphQL, SQL/NoSQL, queues, object storage).
• Experience with CI/CD, distributed systems, and production debugging.
• Strong ownership: can take an ambiguous brief and deliver a working product end to end.
• Clear communication and the ability to work with product, design, and other engineers without hand-holding

Qualifications

• Degree in Computer Science / Engineering or equivalent practical experience.
• Hands‑on engineer with a production-level coding mindset writes, reviews, and ships code.
• Track record of building scalable, reliable systems, not just prototypes or notebooks.
• Good documentation and collaboration skills.
• Bias toward owning the whole problem: product, architecture, implementation, and operations