Senior Java Backend Developer – Java 17+, Spring Boot, Microservices & GenAI/LLM
Toronto, ON - Hybrid (4 Days WFO)
Experience Required: 7+ years
Role Descriptions:
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
• 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 nonfunctional requirements: 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
• 5+ years of strong hands-on experience in Java backend development.
• 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 (e.g., 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
• Prompt templates and 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 OAuth2.0, JWT, SSL/TLS, and secure API patterns.
• Awareness of data privacy, PII handling, and AI governance in regulated environments (BFSI preferred).