Engineering Manager

We are looking for an experienced Engineering Manager - Agentic AI to lead the design, development, and deployment of next-generation AI systems powered by Large Language Models (LLMs), autonomous AI agents, and multi-agent architectures. You will lead a team of AI engineers, backend engineers, and platform developers to build production-grade Agentic AI solutions that automate complex business workflows and deliver intelligent decision-making capabilities. This role requires a combination of technical leadership, people management, software architecture expertise, and hands-on knowledge of modern AI frameworks.

The candidate will have responsibilities across the following functions:

Engineering Leadership:

• Lead and mentor a team of AI Engineers, Backend Engineers, and Full Stack Developers.
• Drive engineering excellence through code reviews, design reviews, and best practices.
• Own project planning, sprint execution, delivery timelines, and engineering quality.
• Collaborate with Product, Design, Data Science, and DevOps teams to deliver AI products.
• Build a high-performance engineering culture focused on innovation and continuous learning.

Agentic AI Development:

• Design and build autonomous AI agents capable of planning, reasoning, memory management, and tool execution.
• Develop multi-agent systems for workflow automation and enterprise use cases.
• Build intelligent orchestration pipelines using modern agent frameworks.
• Implement AI agents with long-term memory, retrieval, planning, and reflection capabilities.
• Develop secure AI systems with human-in-the-loop validation where required.

LLM and AI Platform:

• Integrate commercial and open-source LLMs.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases.
• Design prompt engineering strategies and AI evaluation frameworks.
• Optimise latency, cost, and accuracy of LLM-based applications.
• Build reusable AI SDKs and internal AI platforms.

Backend and Platform Engineering:

• Architect scalable microservices supporting AI workloads.
• Develop REST APIs and event-driven services.
• Design distributed systems capable of handling high-volume AI requests.
• Build observability, monitoring, and logging for AI services.