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).