Senior AI Platform Engineer

Softchoice

Toronto, Ottawa, Calgary

On-site

CAD 112,000 - 140,000

Full time

39 hours ago

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Benefits offered by this job

Health and Wellbeing: Medical and Dent

RRSP/DPSP retirement matching

Paid time off: Vacation & Sick Leave

Employee Discount Program

Job summary

Softchoice Canada is seeking an accomplished AI/ML software engineer to advance our internal AI platform and tooling. You will design, build, and test cloud-native AI solutions for internal use, delivering high-quality APIs, components, and documentation across teams.

You will collaborate with architects, developers and business partners to align AI initiatives with company priorities, leveraging Azure OpenAI, Gen AI, and modern MLOps practices to enable scalable workflows and decision support.

Qualifications

Strong understanding of Agile methodologies applied to AI and Software Engineering.
Experience developing backend services and AI/ML applications using Python.
Experience with Azure OpenAI Service and open-source LLMs.
Experience building Gen AI applications and RESTful APIs.
Knowledge of CI/CD pipelines and IaC (Terraform).
Familiarity with containerization/orchestration (Docker, Kubernetes) in Azure.
Experience with PromptFlow or RAG evaluation matrices.
Expertise in writing/tuning prompts for LLMs.
Experience with indexing/chunking strategies for RAG apps.
Familiarity with Azure AI Search and internal AI platform initiatives.

Responsibilities

Delivering excellence: developing, testing and managing cloud-native tooling and AI-on-cloud tech for internal use.
Design and architect technical solutions for internal employee and business use cases.
Set high standards for architectures, design and operational excellence of internal AI platform.
Create AI solutions to automate workflows and surface insights for decision-makers.
Develop reusable components, services and documentation for other teams.
Collaborate with software developers, QA, and operations to meet internal partner needs.
Engage with SMEs and vendors to refine AI strategy and adoption.
Tailor insights for internal audiences and business teams.
Contribute to R&D of internal AI IP and platform capabilities.
Identify and prioritize internal AI initiatives to boost productivity.