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Added 20 mins ago
Co-Op, Development Operations AI & Automation Enablement Intern
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Mississauga, ON
Full-time
Apply Generative AI technologies, Microsoft Copilot, Power Platform, and other digital tools to automate workflows and business processes.
Added 2 hours ago
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Contract
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Gala Solutions

Artificial Intelligence Engineer

Montreal, QC, Canada

Job type: Contract
Apply by: Oct 30, 2026
Experience: Experience Level: Level 3 (senior): 5-7 years
Posting language: English
Posted: Sep 30, 2026

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Original job description

Job Title: AI Platform Engineering Specialist

Experience Level: Level 3 (senior): 5-7 years

12-month contract position

Location: Montreal (Day 1 onboarding onsite/in-office presence 3x/week)

Our mission is to develop a firmwide Artificial Intelligence (AI) Development Platform that aligns with the firm’s technology principles and drives efficiency and consistency, controls security and strong governance, and promotes innovation, enabling teams to build applications that leverage AI capabilities and accelerate the adoption of AI across our businesses. This role is for a platform engineering specialist who will help build a firmwide AI development platform and drive adoption of AI capabilities throughout the enterprise. We have multiple focus areas across the platform and are looking for energetic, multi-disciplinary candidates who are eager to contribute to providing scalable, secure, enterprise-wide solutions for the firm.

The ideal candidate will have strong hands-on experience building software platforms on any combination of the following platforms - Kubernetes, cloud (AWS, Azure, and/or Google), API-based development, REST framework, data engineering, large-scale API gateway environments, etc. Knowledge of AIML and hands-on experience implementing solutions using generative AI are also preferable. The candidate will have great communication skills, a team-based mentality, and a strong passion for using AI to increase productivity as well as help generate new ideas for product & technical improvements. In the Technology division, we leverage innovation to build the connections and capabilities that power our firm, enabling our clients and colleagues to redefine markets and shape the future of our communities.

Key responsibilities:

Design, build, and operate the AI Gateway's Azure and AWS deployments, taking them from proof of concept to production.
Develop and extend the Python services (FastAPI / Flask) that provide the Gateway's inference, onboarding, and administrative APIs.
Integrate new model providers and model families, including Azure AI Foundry / Azure OpenAI and AWS Bedrock, covering request signing, streaming responses, failover, and quota handling.
Implement cloud-native authentication and secrets handling - Entra ID with Managed Identity and workload federation, AWS IAM roles and STS - with the goal of eliminating stored credentials.
Build and evolve the entitlement and authorization data layer across SQL Server and PostgreSQL, including schema changes, migrations, and data-correctness controls.
Own the platform controls that make the Gateway a governance point: rate limiting, token accounting, content guardrails, audit logging, and chargeback reporting.
Deploy and run the service on Kubernetes (on-premises, AKS, and EKS) using Helm, GitOps, and Terraform, and keep the CI/CD pipelines (Jenkins, GitHub Actions) healthy.
Build the observability to answer any question about a request after the fact - metrics, logs, and dashboards across Prometheus, Grafana, Loki, and Snowflake.
Work with cloud platform, network, and security teams on connectivity, egress policy, network controls, and architecture review, and produce the evidence those reviews require.
Support production: participate in on-call, investigate incidents, and drive fixes and hardening back into the code.
Write tests and documentation as part of delivery, and review peers' changes.

Required qualifications:

Strong, production-grade Python, including a web framework - FastAPI or Flask - and a real testing discipline.
Hands-on Kubernetes: deploying, configuring, and troubleshooting workloads, not solely reading manifests.
Practical OIDC / OAuth 2.0: token validation, JWKS, client-credentials flows, and claim and audience handling.
Microsoft Azure, hands-on across at least three of AKS, Entra ID (app registrations, service principals, Managed Identity / Workload Identity), Azure OpenAI or Azure AI Foundry, Key Vault, Azure Database for PostgreSQL, Azure Cache for Redis, and Azure Monitor.
Amazon Web Services, hands-on across at least three of: IAM and STS / assume-role, SigV4 request signing, Bedrock, EKS, VPC endpoints and private networking, Secrets Manager, and CloudWatch.
Infrastructure as code - Terraform, Bicep, or CDK - and CI/CD with Jenkins or GitHub Actions.
SQL and relational data modeling, including schema migrations.
Clear written and verbal communication and the ability to work directly with security, network, and platform teams.

Preferred qualifications:

Experience building or operating an API gateway, reverse proxy, or multi-tenant platform.
LLM platform engineering specifics: streaming and server-sent events, token accounting, prompt and response guardrails, and model evaluation.
Kafka and Snowflake for audit and consumption data pipelines.
Observability depth: Prometheus and PromQL, Grafana, Loki, OpenTelemetry.
Redis or Valkey beyond basic caching - counters, TTLs, distributed rate-limiter semantics.
Experience delivering in a regulated enterprise environment with corporate proxies, private networking, and strict change control.

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More about this job

Responsibilities

Build, deploy, and operate a secure enterprise AI Gateway across Azure and AWS, including Python APIs, model-provider integrations, cloud-native authentication, and authorization data services. Own governance, observability, Kubernetes deployments, CI/CD, production support, documentation, testing, and collaboration with security, network, and platform teams.

Requirements

Requires production-grade Python and web-framework experience, hands-on Kubernetes, practical OAuth 2.0/OIDC, and experience with multiple Azure and AWS services. Candidates also need infrastructure-as-code and CI/CD experience, SQL data-modeling and migration skills, and strong communication; API gateway, LLM platform, observability, and regulated-enterprise experience are preferred.

Skills

Python
FastAPI
Flask
Kubernetes
Microsoft Azure
Amazon Web Services
OAuth 2.0
OpenID Connect
Terraform
CI/CD
SQL
API Gateway Engineering
Generative AI
Infrastructure as Code
Observability
Cloud Security

Visa sponsorship

Not detected in the job text

Keywords

Python
FastAPI
Flask
Kubernetes
Microsoft Azure
Amazon Web Services
Azure OpenAI
Azure AI Foundry
Amazon Bedrock
Entra ID
AWS IAM
Terraform
Helm
GitOps
Jenkins
GitHub Actions
PostgreSQL
SQL Server
OAuth 2.0
OpenID Connect
API Gateway
Generative AI
Prometheus
Grafana
Loki
Snowflake
Kafka
OpenTelemetry
Redis
Rate Limiting

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