About the role
We are looking for a highly skilled Senior Software Engineer - Backend to build and scale next-generation AI-powered platforms. The ideal candidate will have strong expertise in Java-based backend development, distributed systems, microservices architecture, and hands-on experience building Agentic AI applications using modern LLM frameworks.
You will work closely with Product, AI/ML, and Engineering teams to design intelligent systems capable of autonomous decision-making, task orchestration, reasoning, and workflow automation at scale.
Responsibilities:
- Design, develop, and maintain highly scalable backend services using Java and Spring Boot.
- Architect and build microservices-based applications deployed on cloud-native infrastructure.
- Develop and integrate Agentic AI workflows leveraging Large Language Models (LLMs).
- Build AI agents capable of planning, reasoning, tool usage, and autonomous task execution.
- Implement Retrieval-Augmented Generation (RAG) pipelines and knowledge retrieval systems.
- Develop APIs and backend services to support AI-powered products and platforms.
- Optimise application performance, scalability, reliability, and security.
- Collaborate with Data Scientists, AI Engineers, Product Managers, and Architects to deliver innovative solutions.
- Contribute to system design discussions, code reviews, and engineering best practices.
- Monitor and troubleshoot production systems to ensure high availability and performance.
- Drive technical excellence through automation, testing, observability, and continuous improvement.
The core requirements for the job include the following:
Backend Engineering:
- 5-7 years of software development experience.
- Strong proficiency in Java (Java 8/11/17+).
- Hands-on experience with Spring Boot, Spring Cloud, Hibernate/JPA.
- Strong understanding of microservices architecture and distributed systems.
- Experience designing RESTful APIs and event-driven architectures.
- Expertise with relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, or Redis.
- Strong knowledge of design patterns, data structures, and algorithms.
Agentic AI and GenAI:
- Hands-on experience building Agentic AI applications and AI-powered workflows.
- Experience with LLMs such as GPT, Claude, Gemini, Llama, or similar models.
- Knowledge of RAG architectures, vector databases, and semantic search.
- Experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.
- Understanding of prompt engineering, agent orchestration, memory management, and tool calling.
- Experience integrating AI models through APIs and deploying AI-enabled applications in production.
Cloud and DevOps:
- Experience with AWS, Azure, or GCP.
- Familiarity with Docker and Kubernetes.
- Experience with CI/CD pipelines and infrastructure automation.
- Understanding of monitoring and observability tools.
Good to Have:
- Experience with MCP (Model Context Protocol).
- Knowledge of AI evaluation frameworks and observability platforms.
- Exposure to multi-agent systems and workflow orchestration.
- Experience with Kafka, RabbitMQ, or other messaging systems.
- Familiarity with graph databases and knowledge graphs.
- Contributions to open-source AI projects.
Nice to Have:
- Strong problem-solving and analytical skills.
- Ability to work in a fast-paced, product-focused environment.
- Passion for emerging AI technologies and intelligent systems.
- Excellent communication and stakeholder collaboration skills.
- Ownership mindset with the ability to drive projects independently.