Senior AI Engineer Java

Position Summary

We are seeking an experienced and highly motivated Senior AI Engineer to join the Canada Segments Technology Strategy team. This role will accelerate business and technology transformation initiatives by designing, building, and operating AI systems that enable end-to-end customer experiences, increase straight-through processing, and optimize the overall technology portfolio. The successful candidate will work closely with engineering and business partners to deliver AI systems that are accurate, explainable, safe to operate, and business-ready.

Required Experience & Skills 6-10 years of experience building production software, including recent hands-on delivery of AI or large language model (LLM) systems beyond prototype stage.

Strong JVM engineering experience with Java (Kotlin or Scala preferred).

Solid Software Engineering Practices Including

• Git workflows
• Code reviews
• Automated testing
• Structured logging
• Clean design principles

Hands-on Experience With Modern GenAI Patterns

• Prompt engineering
• Structured/JSON outputs
• Tool and function calling
• Retrieval-Augmented Generation (RAG)
• Agentic workflows

Experience Designing And Querying Graph Or Document Databases

• Neo4j and Cypher, and/or
• MongoDB Atlas
• Experience with vector search and embeddings for semantic retrieval.

Expertise In Agentic systems

Knowledge graphs

AI-enabled document generation and rendering

Cloud infrastructure

AI service stack

CI/CD

Observability

Key Responsibilities

• Design and Build Agentic Analysis Workflows

Design long-running, multi-stage analytical workflows using AI agents that can pause, resume, and recover without losing work.

Orchestrate LLMs and tools including retrieval, calculations, rules, and structured extraction into reliable and traceable workflows.

Engineer prompts and structured-output contracts to ensure validated, machine-usable outputs.

Build on event-sourced service frameworks (e.g., Akka SDK) where state, decisions, and evidence are persisted throughout the workflow.

• Build and Operate the Knowledge Graph

Design and maintain a knowledge graph that stores entities, relationships, and provenance extracted from source content.

Build ingestion and extraction pipelines that transform raw documents into validated, queryable graph structures.

Ensure every fact is traceable to its source.

Implement graph-based and vector retrieval techniques, including GraphRAG and embeddings, to provide grounded responses.

• Expose Capabilities Through Open Agent Interfaces

Develop Model Context Protocol (MCP) tool servers and agent-to-agent (A2A) interfaces.

Integrate with MCP-compatible clients and low-code agent platforms, including Microsoft Copilot Studio agents.

Build conversational interfaces that answer questions strictly from grounded knowledge graph data.

• Establish Grounding, Evaluation, and Safety Standards

Implement anti-hallucination controls ensuring outputs are supported by cited sources.

Build Automated Evaluation Frameworks, Including Section-level scoring

Regression testing

Edge-case coverage

Human-review rubrics

Implement guardrails for prompt injection, data minimization, and safe output generation.

Incorporate human-in-the-loop checkpoints and governance evidence required for AI reviews.

• Production Deployment and Operations

Own the full path from development through production deployment.

Build and maintain CI/CD pipelines, environment configurations, secrets management, and access controls.

Work With Enterprise AI Platform Services, Including Managed model endpoints

Search and retrieval services

Document storage

OCR/document intelligence solutions

Graph databases

Implement logging, metrics, tracing, monitoring, and alerting to support production operations.

Preferred Background Experience delivering enterprise-scale AI systems in production environments.

Experience integrating AI capabilities into business workflows and customer-facing experiences.

Strong collaboration skills with both technical and business stakeholders.

Passion for innovation, continuous improvement, and responsible AI practices.