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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.
Experience
5-8 yrs
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