Xplore Inc. is Canada’s fibre, 5G and satellite broadband company for rural living. Xplore is committed to the relentless pursuit of an improved broadband experience for all Canadians. Xplore is building a world-class fibre optic and 5G wireless network to enable innovative broadband services for better every day rural living, for today and future generations.
As an AI Developer on Xplore's Data & AI team, you will design, build, and deploy the agentic workflows and AI-enabled applications that change how the business runs – across network operations, customer experience, field services, and back-office functions.
There are more problems worth solving here than any single roadmap can hold. That is why this role is built for a systems thinker. You will need to see how systems connect: where a process actually breaks versus where it merely looks broken, which upstream system owns the truth, what a change does three steps downstream, and which piece of scaffolding built today makes the next five workflows cheaper to ship. You will be expected to bring a point of view on sequencing, not just execution.
Unlike a research-oriented ML engineer focused on model development, or a software engineer focused on a single application surface, your specialty is orchestration and integration: decomposing messy, human-shaped processes into reliable, observable, governed agentic systems.
Key Responsibilities Include:
- Partner with process owners across operations, engineering, and corporate functions to map existing workflows end to end, identify high-value automation candidates, and distinguish what should be automated from what should first be redesigned or retired.
- Design, build, and deploy production agentic workflows – tool use, retrieval, multi-step orchestration, and human-in-the-loop checkpoints – that execute real business processes rather than demonstrate them.
- Integrate agentic capabilities into existing and new web applications, building the services, APIs, and user-facing touchpoints through which people interact with agents.
- Define what "working" looks like with stakeholders before building: acceptance criteria, quality thresholds, and measurable process outcomes such as cycle time, human touch rate, error rate, and cost per transaction.
- Own the reliability of everything you deploy: evaluation harnesses, regression testing against real cases, monitoring and tracing, known failure modes, graceful degradation, and clear escalation paths back to humans.
- Establish and enforce guardrails for AI systems in production, including least-privilege data and tool access, sensitive data handling, prompt injection and tool-abuse mitigation, and full auditability of agent actions.
- Build reusable platform components – shared connector and tool libraries, orchestration patterns, prompt and evaluation scaffolding – so each new workflow ships faster and safer than the last.
- Produce architecture documentation, decision records, and operational runbooks so owned systems can be understood, operated, and extended by others.
- Advise leadership on AI opportunity sizing and sequencing, including honest assessment of what is not yet ready for production and why.
The ideal candidate will possess:
- 5+ years of software engineering, data engineering, or applied AI experience, with recent hands-on delivery of production systems.
- Demonstrated experience taking LLM-based or agentic applications into production with real users – beyond prototypes and proofs of concept.
- Strong Python skills, including API design, asynchronous patterns, and service architecture.
- Practical fluency with agentic building blocks: tool and function calling, retrieval, context management, state and memory, multi-step orchestration, and the tradeoffs between competing approaches.
- Sufficient full-stack capability to integrate AI into web applications: REST or GraphQL API design, authentication and authorization, and working competence with a modern front-end framework such as React.
- Systems thinking demonstrated in practice: the ability to reason about coupling, failure propagation, and second-order effects across connected systems, and to defend architectural tradeoffs to both engineers and executives.
- Proven ability to work directly with non-technical stakeholders to elicit requirements from ambiguous, undocumented, or contested processes.
- Sound judgment on security and data handling in AI systems, including access scoping, secrets management, and sensitive data classification.
- Excellent written and verbal communication skills; comfort presenting technical decisions and tradeoffs to Director and VP-level audiences.
Preferred Qualifications
- Experience with agent evaluation and observability tooling, including tracing, offline and online evaluation, and structured human review.
- Experience implementing AI governance, acceptable-use, or model risk frameworks in an enterprise or regulated environment.
- Familiarity with emerging tool-integration standards such as the Model Context Protocol, and with connecting agents to enterprise systems of record.
- Background in telecommunications, network operations, or infrastructure environments.
- Experience with cloud platforms and containerized deployment, and with CI/CD for AI-enabled services.
- Experience working against a governed enterprise data platform such as Databricks, including catalog-based access controls.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
Condition of Employment:
As a condition of employment and in order to comply with industry related data security standards, this position is subject to the successful completion of a Criminal Background Check. Details will be supplied to applicants as they move through the selection process.
Xplore is committed to creating an accessible environment and will accommodate disabilities during the selection process. Please let your recruiter know during the selection process of any accommodation needs.