Senior AI Engineer

Apptoza Inc.; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

Apptoza Inc. is seeking a Senior AI Engineer to lead the design and delivery of an agentic analysis workflow in Canada.

The role coordinates large language models and tools, ensures results are grounded in source evidence, and manages how knowledge is stored and retrieved. You will also build a knowledge graph (Neo4j/MongoDB Atlas) with provenance, enable MCP-based interfaces, and maintain strong safety and evaluation practices across the stack.

Qualifications

• Experience designing and building enterprise-grade AI systems in production.
• Experience integrating AI capabilities into business workflows and customer-facing experiences.
• Strong collaboration with both technical and business stakeholders.
• Commitment to responsible AI, grounding, and safety practices.

Responsibilities

• Design and build the agentic analysis workflow that breaks complex engagements into staged steps.
• Engineer prompts and structured-output contracts to yield validated, machine-usable results.
• Build and operate the knowledge graph storing entities, relationships, and provenance with grounded evidence.
• Expose capabilities through Model Context Protocol (MCP) tool servers and A2A interfaces, enabling external agents and larger workflows.

Job description

- Knowledge Graphs (Neo4j, Cypher, GraphRAG)

We are looking for an experienced and highly motivated Senior AI Engineer to join Canada segment’s Technology Strategy team.

In this role, you will design and build the agentic workflow that breaks a complex analytical engagement into stages, coordinates large language models and tools, and keeps every result grounded in source evidence. You will be accountable for how the system stores and retrieves knowledge, how it exposes its capabilities to other agents and assistants, and how it proves that what it produces is faithful to the source.

Position Responsibilities

1. Design and build the agentic analysis workflow

• Model a long-running, multi-stage analytical workflow as a sequence of AI-agent steps that can pause, resume, and recover without losing work.
• Orchestrate large language models and tools (retrieval, calculation, rules, and structured extraction) into reliable, traceable steps.
• Engineer prompts and structured-output contracts so each step returns validated, machine-usable results rather than free text.
• Build on an event-sourced service framework (for example, the Akka SDK) where state, decisions, and evidence are persisted at every step.

2. Build and operate the knowledge graph

• Design and run the knowledge graph that stores entities, relationships, and provenance extracted from the source corpus (for example, Neo4j or MongoDB Atlas).
• Build the ingestion and extraction pipeline that turns raw documents into a validated, queryable graph, with every fact citing its source.
• Implement graph-based and vector retrieval (GraphRAG and embeddings) so agents answer from grounded evidence rather than memory.

3. Expose capabilities through open agent interfaces

• Build Model Context Protocol (MCP) tool servers and agent-to-agent (A2A) interfaces, so the system's capabilities are available to external assistants and to larger multi-agent workflows.
• Integrate with MCP-compatible clients and low-code agent builders, for example Microsoft Copilot Studio agents, so business users can query the graph and trigger analysis conversationally.
• Build a conversational interface that answers questions strictly from the grounded knowledge graph.

4. Set a high bar for grounding, evaluation, and safety

• Implement anti-hallucination controls: every output statement traces to a cited source, and anything unsupported is flagged or withheld.
• Build automated evaluation (section-level scoring, regression tests, edge-case coverage, and human-review rubrics) and turn it into clear acceptance criteria.
• Add guardrails for prompt injection, data minimization, and safe output, with explicit human-in-the-loop checkpoints, and produce the evidence that AI and model-governance review will ask for.

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.