Description

Key Responsibilities

Design, develop, and maintain scalable applications using Java, Spring Boot, and Microservices architecture.

Lead system design, technical architecture, and solution development for enterprise-grade applications.

Build and support event-driven architectures using Apache Kafka and asynchronous messaging patterns.

Develop secure, scalable RESTful APIs and backend services.

Design and implement Generative AI solutions, including RAG frameworks, AI Agents, vector search, and LLM integrations.

Develop Python-based solutions for AI integrations, automation, and data processing.

Integrate AI/LLM services with existing enterprise applications and microservices.

Collaborate with cross-functional teams to deliver high-quality, cloud-native solutions.

Ensure application performance, reliability, security, and scalability across distributed environments.

Required Qualifications

8+ years of hands-on experience in Java/J2EE application development.

Strong expertise in Java, Spring Boot, and Microservices Architecture.

Proven experience with System Design, Distributed Systems, Design Patterns, and API Architecture.

Hands-on experience with Apache Kafka and event-driven architectures.

Strong Python programming experience, particularly for AI and Generative AI integrations.

Experience designing and implementing Generative AI and LLM-based solutions.

Knowledge of:

Retrieval-Augmented Generation (RAG)

Vector Databases

Embeddings

Prompt Engineering

AI Agents

Experience integrating LLM platforms such as OpenAI, Gemini, Claude, Llama, or similar models.

Strong understanding of REST APIs, authentication, security, scalability, and performance optimization.

Experience working with relational and/or NoSQL databases.

Experience with cloud platforms such as AWS, Azure, or GCP.

Preferred Qualifications

Experience with Docker, Kubernetes, CI/CD pipelines, and modern DevOps practices.

Experience deploying AI-powered enterprise applications in production environments.

Knowledge of MLOps, model lifecycle management, and AI governance practices.

Familiarity with observability, monitoring, and distributed tracing tools

Key Skills

Education

Any Gradute