AI Software Engineer

zenda; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

zenda is seeking an experienced software engineer to build and optimize RESTful services in Java (Spring Boot) to coordinate multi-agent reasoning, planning, and task execution. You will also develop the UI using Angular/Ionic and integrate AI productivity tools into the team's workflow.

The role requires 2-5 years of software engineering experience with AI-driven systems, strong problem-solving skills, and hands-on experience in scalable backend architectures.

Qualifications

• 2-5 years of software engineering experience with AI-driven systems.
• Strong algorithmic thinking and problem-solving abilities.
• Understanding of multi-agent architectures and task coordination.
• Experience with SDLC, system design, and scalable backend architectures.
• Experience building RESTful APIs and UIs for AI and non-AI services.
• Familiarity with AI productivity tools such as Cursor, Antigravity, Claude Code, Codex or equivalents.
• Strong communication and collaboration in fast-moving, experimental environments.
• Experience delivering production-grade apps end-to-end using agentic AI is mandatory.
• Experience with open-source AI models and AI inference engines is a plus.

Responsibilities

• Build and optimise RESTful services in Java (Spring Boot) to coordinate multi-agent workflows.
• Develop the UI for these capabilities using Angular (Ionic).
• Integrate AI productivity tools into daily team workflow to 3-4x development speed.
• Design agentic development environments to automate boilerplate generation, refactor, and documentation for Java and Angular projects.
• Build and deploy evaluation and safety harnesses to benchmark agent-generated code against security standards, performance metrics, and functional unit tests.
• Architect advanced RAG and state management systems that provide agents with precise project-level context.
• Ensure agentic workflows meet production standards for scalability, latency, and token cost-efficiency.
• Conduct code reviews and enforce best practices across AI and software engineering.
• Stay current with advancements in agentic AI, orchestration frameworks, and LLM infrastructure.

Skills

Multi-agent architectures

Systems-level reasoning

Problem solving

Communication & collaboration

Backend systems thinking

Tools

Spring Boot

Angular

RESTful APIs

Cursor

Claude Code

Antigravity

Ionic

Job description

Responsibilities

• Full-Stack Orchestration: Build and optimise RESTful services in Java (Spring Boot) to coordinate multi-agent reasoning, planning, and task execution. Develop the UI for these functionalities using a modern JS framework like Angular (Ionic).


• AI-Driven SDLC: Integrate AI productivity tools (Cursor, Claude Code, and Antigravity) into the team's daily workflow to 3-4x development speed.


• Agentic Dev Setup: Design and maintain specialised agentic development environments that automate boilerplate generation, code refactoring, and documentation for Java and Angular projects.


• Harness Engineering: Build and deploy evaluation and safety harnesses to benchmark agent-generated code against security standards, performance metrics, and functional unit tests.


• Context Engineering: Architect advanced RAG and state management systems that provide agents with precise project-level context, ensuring high-fidelity code generation and minimising token hallucinations.


• System Reliability: Ensure agentic workflows meet production standards for scalability, latency, and token cost-efficiency (utilising semantic caching and prompt optimisation).


• Conduct code reviews and enforce best practices across AI and software engineering.


• Stay current with advancements in agentic AI, orchestration frameworks, and LLM infrastructure.

Requirements

• 2-5 years of experience in software engineering with hands-on exposure to AI-driven systems.


• Strong algorithmic thinking, coding skills, problem-solving skills, and systems-level reasoning.


• Understanding of multi-agent architectures, agent communication patterns, and task coordination.


• Solid understanding of SDLC, system design, and scalable backend architectures.


• Experience building and integrating RESTful APIs and UIs for AI and non-AI services.


• Familiarity with AI developer productivity tools such as Cursor, Antigravity, Claude Code, Codex or equivalent platforms.


• Strong communication and collaboration skills in fast-moving, experimental environments.


• Experience building and deploying production-grade applications end-to-end quickly using agentic AI is mandatory.


• Experience with open-source AI models and AI inference engines is a plus but not mandatory.