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Brunel

Agentic AI Specialist

Toronto, ON, Canada

Full-time

Apply on brunel.net About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Dec 5, 2026
Posting language: English
Posted: Nov 5, 2026

Original job description

We are building AI-assisted automation for a large HR and payroll programme: agents and pipelines that turn business requirements and test-case workbooks into test cases, validated staging data and reviewer-ready evidence. The core is already working and in use. We need a hands-on AI engineer to build, host and enhance these agents with us.

Build agents. Design and implement LLM-based agents and tools: tool and function calling, structured outputs, multi-step workflows, human-in-the-loop approval, prompt and context design.
Build the tool layer. Extend our MCP (Model Context Protocol) server and APIs that agents call to read requirements, agreements, process maps, reference test cases and product documentation.* Host and operate. Take agents from a local, single-user setup to a hosted service: containerised backend, identity and access control, audit logging, secrets management, deployment on Azure.
Retrieval and knowledge. Build retrieval over HR and pay documents (collective agreements, process catalogues, test libraries): chunking, embeddings, hybrid search, re-ranking, with measured retrieval quality.* Evaluate. Build labelled evaluation sets and regression tests for every AI feature; report accuracy, precision and recall; keep deterministic logic deterministic where a rule beats a prompt.
Integrate. Connect outputs to Azure DevOps test management, and build Microsoft 365 agents (Copilot Studio, Agent Builder, Power Automate, Teams, SharePoint) for reviewer feedback and Q&A inside the tenant.* Deliver with the tooling. Run the pipelines on new requirement workbooks, resolve what they flag, reconcile counts, and prepare outputs for review.
Bring ideas. Propose and prototype new uses of AI across HR and pay testing: coverage analysis, scenario generation, defect triage, process-to-requirement mapping.

Python, 5+ years. Production code on a shared codebase: typing, pytest, git branches and pull requests, code review.* LLM application engineering, 12+ months shipped. Direct experience with at least one major model API (Anthropic, OpenAI, Azure OpenAI): tool use, structured output, streaming, token and cost management, prompt caching.
Agentic systems. Built multi-step agents with tools, memory and approval gates; familiar with MCP and at least one agent framework or SDK.* Retrieval (RAG). Embeddings, vector stores, hybrid search, chunking strategy, and how to evaluate retrieval before building on it.
Evaluation. Labelled datasets, metrics, regression suites for non-deterministic systems; can explain why a number moved.* Backend and hosting. FastAPI or equivalent, Docker, REST, authentication and authorisation, logging, deployment to Azure (App Service, Container Apps or AKS), CI/CD.
Data handling. Messy multi-sheet Excel and CSV at scale with openpyxl or pandas; reconciliation that adds up; SQL.* AI-assisted development. Works fluently with AI coding assistants day to day and reviews their output critically. Nice-to-have skills* Azure DevOps: test plans, suites, work items, REST API.
HCM or payroll platforms (Dayforce, Workday, SAP SuccessFactors, PeopleSoft) or public-sector HR and pay.

Why work with Brunel? We are proud to offer exciting career opportunities from over 100 offices globally in 42 countries. Advancing your career takes time and effort - let us match you to your ideal position.

Apply on brunel.net About employer

More about this job

Responsibilities

The Agentic AI Specialist at Brunel will build LLM agents and their tools, host them as a service on Azure, and set up retrieval and evaluation for HR and pay testing. The role also runs the pipelines on new requirement workbooks and proposes new uses of AI.

Requirements

Candidates need five-plus years of Python, over twelve months shipping LLM applications with a major model API, and experience with agentic systems, retrieval (RAG) and evaluation. Backend hosting on Azure, Docker and CI/CD are also required.

Skills

Python
LLM application engineering
Tool and function calling
Structured outputs
Model Context Protocol (MCP)
Agent frameworks and SDKs
Anthropic API
OpenAI API
Azure OpenAI
Prompt caching
Retrieval-augmented generation (RAG)
Embeddings
Vector stores
Hybrid search
Re-ranking
Chunking strategy
Evaluation datasets and regression testing
FastAPI
Docker
REST APIs
Authentication and authorisation
Azure App Service
Azure Container Apps
Azure Kubernetes Service
CI/CD

Keywords

Agentic AI
LLM
Python
Model Context Protocol
MCP
Anthropic
OpenAI
Azure OpenAI
RAG
retrieval
embeddings
vector search
hybrid search
FastAPI
Docker
Azure
Azure Container Apps
Azure DevOps
Copilot Studio
Power Automate

Seniority

Senior

Tools

Azure DevOps
Copilot Studio
Power Automate
Microsoft Teams
SharePoint
Docker
openpyxl
pandas

Languages

English

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Jobs near Toronto, ON