Full Stack Engineer
Senior full-stack engineer at a technology consulting firm, building Java/Spring Boot backends with React or Angular frontends for enterprise clients, and taking AI capabilities to production via LLM integration, RAG pipelines, and MCP-based connections to enterprise systems.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Fullstack
- Posted
Skills
Must have
- 5+ years professional software development with strong hands-on Java and Spring Boot expertise
- Strong REST API design, microservices, OOP, and design patterns
- Relational databases (PostgreSQL or MySQL) including SQL development and performance tuning
- Hands-on LLM-powered application development beyond basic chatbot integrations
- Practical RAG implementation: embeddings, vector databases, retrieval strategies, metadata filtering
- Implementing or integrating MCP servers/clients with tool discovery, invocation, and access controls
- Prompt design, tool/function calling, structured outputs, model context management
- Familiarity with vector search platforms (Qdrant, pgvector, Pinecone, OpenSearch, or equivalent)
- Git, automated testing, Docker, and CI/CD pipelines
- Strong problem-solving, communication, and collaboration skills
Nice to have
- Spring AI, LangChain4j, or comparable Java AI frameworks
- Python for AI services, data processing, or orchestration
- Amazon Bedrock, Azure OpenAI, Google Vertex AI, or comparable AI platforms
- Agent frameworks such as LangGraph, Semantic Kernel, or AWS Strands
- Hybrid search, reranking, and retrieval evaluation
- Prompt injection risks, sensitive data protection, safe execution of AI-invoked tools
- Kubernetes, messaging platforms such as Kafka, and cloud deployment
- Human approval steps, retries, audit logs, and observability for AI workflows
Full description
Senior Full Stack Engineer – Java, Spring Boot & AI
About Code17
Code17 provides technology consulting and professional services across data engineering, cloud, full-stack development, and agentic AI. We help enterprises build modern applications and implement AI solutions that improve business operations.
Role Overview
We are seeking a Senior Full Stack Engineer with strong Java and Spring Boot expertise and hands‑on experience building AI-powered applications. The ideal candidate can develop applications across the frontend and backend while integrating large language models, Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP).
This role requires practical software engineering experience, including taking AI capabilities from prototypes into secure, reliable production applications.
Key Responsibilities
- Design, develop, and maintain applications using Java, Spring Boot, and React or Angular.
- Build REST APIs, microservices, and integrations with enterprise systems.
- Develop RAG pipelines covering document ingestion, parsing, chunking, embeddings, vector retrieval, reranking, and grounded response generation.
- Integrate LLMs through cloud AI platforms or model provider APIs.
- Build and integrate MCP servers and clients to connect AI applications with enterprise tools, APIs, and data sources.
- Implement AI workflows using tool calling, structured outputs, and orchestration frameworks.
- Apply authentication, authorization, and data access controls across application and AI workflows.
- Evaluate AI response quality, retrieval accuracy, latency, and cost; troubleshoot failures and improve performance.
- Write automated tests, participate in code reviews, and support CI/CD and production releases.
- Collaborate with product owners, architects, and business stakeholders to translate requirements into working solutions.
Required Qualifications
- 5+ years of professional software development experience, with strong hands‑on Java and Spring Boot expertise.
- Strong understanding of REST API design, microservices, object‑oriented programming, and design patterns.
- Experience with relational databases such as PostgreSQL or MySQL, including SQL development and performance tuning.
- Hands‑on experience developing LLM-powered applications beyond basic chatbot integrations.
- Practical experience implementing RAG, including embeddings, vector databases, retrieval strategies, and metadata filtering.