Senior Platform Engineer – Data & AI

Equinix • Toronto, ON

1d • $131,000 - $181,000 • Onsite

About The Position

We are seeking a highly skilled Senior Platform Engineer – Data & AI to architect and build next-generation AI-native and Agentic platforms that power enterprise-scale data, automation, and intelligent systems. This role goes beyond traditional data platforms to focus on Agentic AI ecosystems, including multi-agent orchestration, agent lifecycle management, agent communication protocols, and AI-driven platform automation. You will design and operate a unified platform that supports: Data pipelines and real-time streaming APIs and microservices GenAI and LLM-powered applications Agentic workflows and multi-agent systems Working closely with AI/ML engineers, platform teams, SRE, and product teams, you will help build a scalable, observable, and governed AI platform on Google Cloud, leveraging automation, IaC, and modern cloud-native patterns.

Requirements

• 8–12 years of experience in Platform Engineering, Data Engineering, Cloud Architecture, or AI Platform Engineering
• Proven experience building enterprise-scale data and AI platforms
• Strong programming expertise in Java, Python, Full-Stack and SQL
• Experience building microservices and API-driven architectures
• Deep understanding of distributed systems and cloud-native design
• Strong experience with Google Cloud Platform (GCP) (mandatory)
• Hands-on experience with: Kubernetes and containerized workloads, Terraform and Infrastructure-as-Code, CI/CD pipelines and GitOps
• Experience with Kafka, Pub/Sub, Spark, Flink, or similar systems
• Strong background in real-time and batch data processing
• Hands-on experience with: LLM frameworks and APIs, Multi-agent orchestration frameworks (CrewAI, LangGraph, AutoGen, etc.), RAG pipelines and vector databases
• Experience building or working with: Agent Gateway architectures, A2A communication models, MCP or context-sharing frameworks, Agent Development Kits (ADKs)
• Experience building full stack applications with modern frontend frameworks (React, Angular, Vue.js).
• Strong understanding of REST/GraphQL APIs and UI integration patterns.
• Experience with real-time UI updates using WebSockets or streaming architectures.
• Familiarity with design systems, UX principles, and responsive design.
• Experience building platform dashboards, developer portals, or observability UIs.
• Experience with observability tools: Prometheus, Grafana, OpenTelemetry
• Strong debugging and system analysis skills.
• Familiarity with AI/LLM observability and evaluation frameworks.
• Experience with: Data catalogs and metadata platforms, Data quality and lineage frameworks, Semantic modeling and data governance

Nice To Haves

• Experience with Vertex AI, MLflow, Kubeflow, or ML platforms.
• Prior implementation of data mesh or data fabric architectures.
• Experience with Looker Modeler / LookML or semantic layers.
• Exposure to AI safety, governance, and responsible AI practices.
• Experience building enterprise AI/Agentic platforms at scale.

Responsibilities

• Architect and build cloud-native platforms on Google Cloud (GCP) supporting data, AI, and agentic workloads
• Design event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent systems
• Build scalable microservices and APIs using modern frameworks (e.g., Java, Spring Boot)
• Develop and manage real-time and batch data pipelines using Airflow, Dataform, Dataflow, Spark, or similar tools
• Implement Infrastructure-as-Code (IaC) using Terraform and Kubernetes for scalable, repeatable deployments
• Enable platform automation using CI/CD, GitOps, and self-service frameworks
• Ensure platform scalability, reliability, and cost efficiency
• Design and build Agentic Platforms that support: Agent lifecycle management, Task orchestration, Context and memory handling
• Develop and orchestrate multi-agent systems using frameworks such as CrewAI, LangGraph, AutoGen, or equivalent
• Implement agent communication and coordination patterns across distributed systems
• Build and integrate: Agent Gateway for managing agent interactions and routing, A2A (Agent-to-Agent) communication protocols, MCP (Model Context Protocol) or equivalent for context sharing and orchestration, ADK (Agent Development Kits) or internal frameworks for rapid agent development
• Enable use cases such as: Autonomous pipeline monitoring and remediation, AI-assisted platform operations, Intelligent workflow automation, Code and data pipeline generation
• Integrate LLMs and GenAI services (e.g., OpenAI, Gemini, Claude) into platform workflows.
• Build and support: RAG pipelines and retrieval systems, Vector search and embedding architectures (Weaviate, Pinecone, FAISS)
• Enable AI-driven automation for: Platform operations, Data quality monitoring, Incident analysis and resolution
• Develop reusable AI platform services and APIs for enterprise consumption.
• Design and implement Agent Observability frameworks, including: Agent execution tracing, Decision tracking and explainability, Latency and performance monitoring, Failure and retry analysis
• Integrate observability using tools like: OpenTelemetry, Prometheus, Grafana, AI/LLM observability tools (e.g., prompt tracing, evaluation frameworks)
• Enable end-to-end observability across data pipelines, APIs, and agent workflows.
• Lead initiatives in: Data modeling and semantic layer design, Data cataloging and metadata management, Data quality and lineage tracking
• Implement governance frameworks using tools such as DataHub, Collibra, or equivalent.
• Support data mesh and data fabric architectures for federated data ownership.
• Build automation-first platforms leveraging: AI-driven workflows, Self-healing systems, Event-driven automation
• Use GenAI to: Automate operational tasks, Generate platform configurations and code, Enhance developer productivity
• Collaborate with SRE and Production Support teams to improve: Reliability, Incident response, Operational efficiency
• Develop platform SDKs, CLIs, and reusable blueprints.
• Enable self-service platform capabilities for engineering teams.
• Standardize best practices for: APIs, Data pipelines, Agent development
• Mentor engineers and promote a culture of innovation and continuous learning.

Benefits

• Employee Assistance Program
• Healthcare coverage that is designed to complement the provincial healthcare system
• Life, disability and optional benefit plans
• Defined Contribution Pension Plan (DCPP)
• Group Retirement Savings Plan (RRSP)
• Tax-Free Savings Plan (TSFA)
• Vacation and personal time
• Various paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed