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Added 20 mins ago
Artificial Intelligence Engineer
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Contract
Support production: participate in on-call, investigate incidents, and drive fixes and hardening back into the code.
Added 20 mins ago
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Full-time
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Added 16 hours ago
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Added 1 day ago
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Added 1 day ago
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Added 1 day ago
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Added 1 day ago
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Added 1 day ago
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Added 1 day ago
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Randstad Enterprise

AI Platform Engineering Specialist

Montreal, QC, Canada

Job type: Full-time
Apply by: Oct 24, 2026
Posting language: English
Posted: Sep 24, 2026

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Applications open in a new tab.

Original job description

Job Title: AI Platform Engineering Specialist

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

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, 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, 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, CloudWatch.
Infrastructure as code - Terraform, Bicep or CDK - and CI/CD with Jenkins or GitHub Actions.
SQL and relational data modelling, 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, 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

Design, build, and operate the AI Gateway across Azure and AWS, including its Python APIs, model-provider integrations, authentication, authorization, governance controls, and data layer. Deploy and support the platform on Kubernetes, maintain CI/CD and observability, collaborate with security and infrastructure teams, and participate in production on-call and incident response.

Requirements

Requires production-grade Python and web-framework experience, hands-on Kubernetes, practical OIDC/OAuth 2.0, and experience with at least three specified services in each of Azure and AWS. Also requires infrastructure-as-code and CI/CD experience, SQL data modelling and migrations, testing discipline, and strong communication; API gateway, LLM platform, observability, and regulated-enterprise experience are preferred.

Skills

Python
FastAPI
Flask
Kubernetes
OAuth 2.0
Microsoft Azure
Amazon Web Services
Terraform
CI/CD
SQL
API Gateway
LLM Platform Engineering
Observability
Prometheus
Grafana
PostgreSQL

Visa sponsorship

Not detected in the job text

Keywords

Python
FastAPI
Flask
AI Gateway
Azure
AWS
Azure AI Foundry
Azure OpenAI
Amazon Bedrock
Kubernetes
AKS
EKS
Helm
GitOps
Terraform
Jenkins
GitHub Actions
OIDC
OAuth 2.0
Entra ID
Managed Identity
AWS IAM
STS
SigV4
SQL Server
PostgreSQL
Prometheus
Grafana
Loki
Snowflake

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