AI Platform Engineering Specialist

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.