AI Software Engineer

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.

This job was posted by Meenal Sharma from Zenda.