Advanced AI Engineer — Global AI Agent Ecosystem & Complete Project Execution

Company: Nova AI

Position: Senior AI Agent Engineer / Autonomous Systems Architect

Work Arrangement: 100% Remote

Hiring Priority: Urgent

Project Focus: End-to-end AI applications for clients

Mission

Research, evaluate, and leverage the global AI agent ecosystem to engineer complete, reliable, and commercially useful AI applications. The engineer must be capable of selecting the best available agents, models, frameworks, and development tools for each project rather than depending on a single AI platform.

Global AI Agent Ecosystem Expertise The candidate should maintain broad, continuously updated knowledge of relevant AI agents and tools, including:

- AI coding agents: OpenAI Codex, Claude Code, GitHub Copilot, Gemini-based coding tools, and other suitable coding agents.
- General-purpose task agents: Research, browser-based execution, document generation, data analysis, and multi-step task automation systems.
- Agent development frameworks: OpenAI Agents SDK, LangGraph, LangChain, CrewAI, Google ADK, AutoGen and comparable frameworks.
- Browser and computer-use agents: Tools capable of interacting with websites, browser interfaces, and supported desktop workflows.
- Business automation agents: n8n, workflow automation platforms, API-connected agents, and enterprise automation systems.
- Knowledge and research agents: RAG systems, document-search agents, retrieval pipelines, and research assistants.
- Multimodal agents: Systems combining text, images, audio, video, or other supported modalities.
- Open-source and self-hosted agents: Tools suitable for privacy-sensitive, cost-conscious, or customized deployments.
- Agent communication and integration: MCP, API integrations, event-driven workflows, and interoperable tool interfaces.

Full-Project Engineering Responsibilities The engineer must be able to:
- Investigate client requirements and identify the actual business problem.
- Research available agents and compare their capabilities, limitations, licensing, cost, and security.
- Design an architecture that combines AI agents with conventional software where appropriate.
- Develop complete frontend, backend, database, API, and AI functionality.
- Coordinate multiple agents through controlled workflows with explicit permissions and success criteria.
- Test generated code, validate model outputs, identify failures, and implement recovery mechanisms.
- Deploy the application, configure monitoring, document the system, and demonstrate its operation.
- Deliver source code, setup instructions, test results, and client handover documentation.




- Continue evaluating emerging tools and replace components when evidence shows a meaningful improvement.

Required Standard of Execution The engineer should demonstrate the ability to turn an approved client requirement into a functional application with clear milestones, measurable acceptance criteria, and realistic delivery estimates. The role requires independent problem-solving, practical engineering skill, technical research, and end-to-end ownership. AI agents must be treated as engineering tools whose outputs require validation—not as infallible systems.

Candidate Evaluation

Applicants should demonstrate

- A working application built using one or more AI agents.
- A documented example of coordinating multiple agents or tools.
- Evidence of personally implemented and tested software.
- The ability to explain why particular agents were selected over alternatives.
- A reproducible deployment process, security considerations, and known limitations.

Success criterion: Deliver the best practical solution for each client—not the largest number of agents or frameworks in a project.Advanced AI Engineer — Global AI Agent Ecosystem & Complete Project Execution

Company: Nova AI

Position: Senior AI Agent Engineer / Autonomous Systems Architect

Work Arrangement: 100% Remote

Hiring Priority: Urgent

Project Focus: End-to-end AI applications for clients

Mission

Research, evaluate, and leverage the global AI agent ecosystem to engineer complete, reliable, and commercially useful AI applications. The engineer must be capable of selecting the best available agents, models, frameworks, and development tools for each project rather than depending on a single AI platform.

Global AI Agent Ecosystem Expertise The candidate should maintain broad, continuously updated knowledge of relevant AI agents and tools, including:

- AI coding agents: OpenAI Codex, Claude Code, GitHub Copilot, Gemini-based coding tools, and other suitable coding agents.
- General-purpose task agents: Research, browser-based execution, document generation, data analysis, and multi-step task automation systems.
- Agent development frameworks: OpenAI Agents SDK, LangGraph, LangChain, CrewAI, Google ADK, AutoGen and comparable frameworks.
- Browser and computer-use agents: Tools capable of interacting with websites, browser interfaces,



and supported desktop workflows.
- Business automation agents: n8n, workflow automation platforms, API-connected agents, and enterprise automation systems.
- Knowledge and research agents: RAG systems, document-search agents, retrieval pipelines, and research assistants.
- Multimodal agents: Systems combining text, images, audio, video, or other supported modalities.
- Open-source and self-hosted agents: Tools suitable for privacy-sensitive, cost-conscious, or customized deployments.
- Agent communication and integration: MCP, API integrations, event-driven workflows, and interoperable tool interfaces.

Full-Project Engineering Responsibilities The engineer must be able to:
- Investigate client requirements and identify the actual business problem.
- Research available agents and compare their capabilities, limitations, licensing, cost, and security.
- Design an architecture that combines AI agents with conventional software where appropriate.
- Develop complete frontend, backend, database, API, and AI functionality.
- Coordinate multiple agents through controlled workflows with explicit permissions and success criteria.
- Test generated code, validate model outputs, identify failures, and implement recovery mechanisms.
- Deploy the application, configure monitoring, document the system, and demonstrate its operation.
- Deliver source code, setup instructions, test results, and client handover documentation.
- Continue evaluating emerging tools and replace components when evidence shows a meaningful improvement.

Required Standard of Execution The engineer should demonstrate the ability to turn an approved client requirement into a functional application with explicit milestones, measurable acceptance criteria, and realistic delivery estimates. The role requires independent problem-solving, practical engineering skill, technical research, and end-to-end ownership. AI agents must be treated as engineering tools whose outputs require validation—not as infallible systems.

Candidate Evaluation

Applicants should demonstrate

- A working application built using one or more AI agents.
- A documented example of coordinating multiple agents or tools.
- Evidence of personally implemented and tested software.
- The ability to explain why particular agents were selected over alternatives.
- A reproducible deployment process, security considerations, and known limitations.

Success criterion: Deliver the best practical solution for each client—not the largest number of agents or frameworks in a project. Pay: ₹10,000.00 - ₹62,000.00 per month

Work Location: Hybrid remote in Ahmedabad, Gujarat

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