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Senior AI Engineer – Agentic AI Platform, LLM & GraphRAG
Job Location:
Monthly Salary:
Experience Required:
Posted:
1 September 2026 (9 days ago)
Posted:
Application Deadline:
29 November 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Senior AI Engineer
Location: Toronto ON Hybrid (3 days onsite: Tuesday-Thursday 8:30 AM to 5:00 PM)
We are seeking an experienced and highly motivated Senior AI Engineer to join the Technology Strategy team supporting transformative AI initiatives. This role is focused on designing building and operating advanced AI systems that enable end-to-end customer experiences increase automation and straight-through processing and accelerate digital transformation objectives.
The ideal candidate brings deep expertise in agentic AI systems knowledge graphs AI-powered document generation cloud infrastructure AI services CI/CD observability and enterprise-scale deployments. You will partner closely with business and engineering stakeholders to deliver AI solutions that are accurate explainable secure scalable and production-ready.
This is a unique opportunity to help shape the future of AI-driven business transformation while working with cutting-edge technologies and modern AI architectures.
Key Responsibilities:
Design and Build Agentic AI Workflows
Architect and develop long-running multi-stage AI-driven analytical workflows that can pause resume recover and maintain state throughout execution.
Orchestrate large language models (LLMs) alongside tools for retrieval reasoning calculations business rules and structured data extraction.
Develop robust prompt engineering strategies and structured output frameworks to ensure validated machine-consumable responses.
Build solutions on event-driven and event-sourced architectures ensuring decisions evidence workflow states and results are persisted throughout the process.
Deliver transparent and traceable AI workflows that provide explainability and auditability.
Build and Operate Enterprise Knowledge Graphs Design implement and maintain knowledge graph solutions using technologies such as Neo4j MongoDB Atlas or similar platforms.
Develop ingestion extraction and transformation pipelines that convert unstructured documents into validated and searchable knowledge models.
Ensure all extracted data includes provenance and source attribution.
Implement GraphRAG vector search embeddings and hybrid retrieval strategies to provide grounded and evidence-based AI responses.
Optimize graph architecture for performance scalability and business usability.
Develop Open Agent Interfaces and AI Integrations Build Model Context Protocol (MCP) tool servers and Agent-to-Agent (A2A) interfaces that allow AI capabilities to be consumed by external systems and multi-agent ecosystems.
Integrate AI services with Microsoft Copilot Studio low-code platforms and conversational AI solutions.
Design conversational interfaces that leverage grounded enterprise knowledge while maintaining response accuracy and trustworthiness.
Enable business users to query enterprise knowledge assets and trigger analytical workflows through natural language interactions.
Establish AI Governance Evaluation and Safety Standards Implement comprehensive anti-hallucination controls to ensure all AI-generated outputs are sourced validated and explainable.
Build evaluation frameworks including regression testing section-level scoring edge-case validation and human-review workflows.
Create measurable acceptance criteria and quality standards for AI solutions.
Implement guardrails for prompt injection protection secure data handling privacy compliance and safe output generation.
Partner with governance and risk teams to produce documentation and evidence required for AI model reviews and compliance processes.
Productionize and Manage AI Platforms
Own end-to-end deployment processes from development through production.
Develop and maintain CI/CD pipelines infrastructure automation environment configuration secrets management and access controls.
Manage integrations with cloud-based AI services including:
- Foundation and hosted models
- Search and retrieval services
- Document repositories
- OCR and document intelligence platforms
- Vector databases and knowledge graph solutions Establish comprehensive observability practices including logging monitoring distributed tracing alerting and operational analytics.
Drive reliability scalability and operational excellence across AI platforms and services.
Required Qualifications
Bachelors or Masters degree in Computer Science Software Engineering Artificial Intelligence Data Science or a related field.
7 years of software engineering experience with at least 3 years focused on AI/ML solutions.
Strong expertise in building and deploying LLM-powered applications and agentic AI systems.
Experience designing and implementing knowledge graphs GraphRAG architectures vector databases and retrieval-augmented generation solutions.
Hands-on experience with modern AI frameworks and orchestration tools.
Strong knowledge of cloud platforms such as Azure AWS or Google Cloud.
Experience building APIs agent interfaces and distributed systems.
Expertise with CI/CD pipelines containerization infrastructure automation and DevOps practices.
Strong understanding of observability monitoring logging and production support.
Experience implementing AI governance evaluation methodologies and responsible AI practices.
Excellent communication and stakeholder management skills.
Preferred Qualifications
Experience with Akka SDK or other event-sourced architectures.
Experience with Neo4j MongoDB Atlas GraphRAG vector search and embeddings.
Knowledge of Model Context Protocol (MCP) and Agent-to-Agent (A2A) frameworks.
Experience with Microsoft Copilot Studio or conversational AI platforms.
Familiarity with enterprise AI governance and regulatory compliance requirements.
Experience working in financial services insurance or highly regulated industries.
Required Skills:
Top 3 Required Skills: 1. IBM Financial transaction 2. Payment flow 3. Support Modernization Detailed Job Description: Design develop and maintain applications built on IBM Financial Transaction Manager (FTM) to support core payments processing. Contribute to the development of payment flows supporting transaction processing. Build and support integrations between FTM and upstream/downstream systems using enterprise integration patterns. Participate in the design development testing deployment and production support. Troubleshoot and resolve application and integration issues in a complex regulated environment. Collaborate with architecture QA and operations teams to ensure platform stability scalability and performance. Support modernization initiatives and enhancements to existing payment hub capabilities. Produce clear technical documentation and participate in code reviews and knowledge sharing.
