Java Backend Developer - Java (17+), Spring Boot, Microservices

Java BE Developer – Java Spring Boot, Microservices

Location: Toronto, ON – Hybrid (4 Days WFO)

Key Responsibilities

• Design, develop, and maintain high-performance backend services using Java (17+), Spring Boot, and Microservices architecture.
  
• Build and expose RESTful and event-driven APIs supporting enterprise-scale applications.
  
• Integrate Generative AI / LLM capabilities (e.g., text generation, summarization, Q&A, classification) into backend workflows.
  
• Design, test, and optimize prompts and prompt orchestration strategies to ensure accuracy, determinism, and performance.
  
• Develop AI-aware backend components such as prompt templates and prompt pipelines, Retrieval Augmented Generation (RAG) services, and AI inference orchestration layers.
  
• Implement secure API integrations with AI platforms and internal data sources, ensuring compliance with enterprise security standards.
  
• Apply prompt versioning, evaluation, and monitoring techniques to improve AI output quality over time.
  
• Ensure non-functional requirements including scalability, resiliency, performance, and observability.
  
• Contribute to CI/CD pipelines, containerization, and cloud-native deployments.
  
• Participate in code reviews, architecture discussions, and technical design decisions.
  
• Support production systems and troubleshoot complex backend or AI integration issues.
  

Required Technical Skills

Core Backend Engineering

• Strong hands-on experience in Java backend development (5+ years).
  
• Expertise in Java 11/17+, Spring Boot, Spring MVC, Spring Security.
  
• Solid experience in Microservices, REST APIs, and API design (OpenAPI/Swagger).
  
• Experience with containers and cloud platforms (Docker, Kubernetes, OpenShift, Azure/AWS).
  
• Strong knowledge of SQL and NoSQL databases (DB2, PostgreSQL, MongoDB).
  
• Experience in CI/CD, DevOps practices, and automated testing.
  

AI & Prompt Engineering

• Hands-on experience integrating Large Language Models (LLMs) into backend systems.
  
• Strong understanding of prompt engineering techniques including zero-shot, few-shot, and chain-of-thought prompting.
  
• Experience with prompt templates, dynamic prompt generation, guardrails, validation, and hallucination reduction.
  
• Experience building RAG-based solutions using vector stores and embeddings.
  
• Familiarity with AI orchestration frameworks or SDKs (enterprise or open source).
  
• Ability to evaluate prompt and model responses for quality, bias, and consistency.
  

Security & Compliance

• Experience implementing OAuth 2.0, JWT, SSL/TLS, and secure API patterns.
  
• Awareness of data privacy, PII handling, and AI governance in regulated environments (BFSI preferred).