Lead AI Engineer - Full Stack

About the Role :

We are looking for high-potential Computer Science graduates from IITs, working in industry, who are passionate about building next-generation intelligent applications. This role focuses on developing full-stack systems powered by cloud-native architectures and agentic AI frameworks, enabling autonomous, adaptive, and scalable digital solutions.

As part of our engineering team, you will work on cutting-edge problems at the intersection of software engineering, cloud platforms, and AI-driven autonomous systems (agents).

Key Responsibilities :

• Design and build end-to-end full-stack applications across frontend, backend, and APIs.

• Develop cloud-native solutions leveraging modern platforms such as Azure, AWS, and GCP.

• Build and deploy agentic AI applications, including multi-agent systems and autonomous workflows.

• Implement scalable backend systems using microservices and event-driven architectures.

• Work with LLM-based frameworks and agent orchestration tools to create intelligent workflows.

• Ensure best practices in code quality, testing, debugging, observability, performance optimization, security, and scalability.

• Collaborate with cross-functional teams to translate business problems into technical solutions.

• Participate in design reviews and contribute to architectural decisions.

Required Qualifications :

• B. Tech in Computer Science, or related disciplines, from IITs.

• Strong foundation in Data Structures & Algorithms, Object-Oriented Programming, Operating Systems, DBMS, and Networking.

• Proficiency in full stack programming with understanding of application development & deployment.

Technical Skills :

• Full Stack Development : Frontend : React, Angular, Vue, or any modern JavaScript framework. Backend : Node.js, Java Spring Boot, Python FastAPI, or Django. Strong understanding of RESTful APIs and microservices design.

• Cloud & DevOps : Hands-on exposure to Azure, AWS, or GCP. Understanding of containers using Docker, CI/CD pipelines, and basic Kubernetes concepts.

• Agentic AI & Modern AI : Experience through academic work, projects, internships, or industry exposure in building AI agents or workflows using LLMs. Familiarity with LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar frameworks. Understanding of prompt engineering, RAG, tool usage, and agent orchestration patterns.

What We Look For :

• Strong problem-solving mindset and curiosity to learn emerging technologies.