Visier Solutions · Vancouver
Software Engineeringposted 5w ago
Visier is the global leader in Workforce Intelligence that powers every people decision. We bring Workforce AI to life for HR departments through our award-winning, agentic AI technology by surfacing the insights leaders need to plan, decide, and act with confidence in the moments that matter most. As the market leader in people analytics, workforce planning, organizational design, and manager effectiveness solutions, we fuel smarter decision-making for organizations across the globe. Our mission is to help businesses lead with insight at scale as they continuously transform.
Founded in 2010 by the pioneers of business intelligence, we have over 85,000 customers in 75 countries—including enterprises like BASF, Panasonic, Domino’s Pizza, Experian, Amgen, eBay, and Ford Motor Company.
Position Overview
Visier is building the centralized control and data plane—the infrastructure, pipelines, and governance layer—that powers our internal AI transformation (Vector) across professional services, customer success, and internal knowledge. As the Staff DevOps Developer on this initiative, you will own the platform and integration layer that the entire system runs on. You will define how cloud infrastructure is designed, write production-grade Python application code, and build the Model Context Protocol (MCP) servers and RAG retrieval services that enable AI tools and agents to reliably query and act on organizational knowledge.
In this role, you will bring a blend of software engineering discipline, deep cloud networking and Infrastructure as Code (IaC) expertise, and an eye for emerging AI agent architecture. You will evaluate emerging agent frameworks, set platform-wide engineering standards, and engineer secure, resilient architectures designed to scale.
Platform & Network Architecture: Define and operate a multi-cloud infrastructure across AWS and Azure—specifying compute, storage, VPCs, subnets, private endpoints, and load balancing with clear architectural rationale.
Production Python & Integration Layer: Write tested, maintainable Python application code, build resilient API integrations with core source systems (Salesforce, ServiceNow, Gong, Gainsight), and develop internal tooling and automation workflows.
RAG Context Engine & MCP Servers: Design, build, and optimize the inference-time retrieval service—from query embedding and vector search to re-ranking—and expose this via Model Context Protocol (MCP) servers for governed agent access.
Infrastructure as Code & Modern CI/CD: Own platform infrastructure using Terraform across environments and establish automated CI/CD pipelines (Jenkins, Bitbucket, Artifactory) to deploy platform services and data pipeline artifacts.
AI Tooling & Agent Skill Integration: Define and lead the integration layer between the data warehouse and AI assistants, developing agent skill definitions, query APIs, and optimized prompt structures for reliable agent execution.
AI System Security & Platform Operations: Embed foundational security controls—secrets management, least-privilege IAM, network segmentation, and defenses against prompt injection—while establishing robust monitoring, cost governance, and observability.
Education & Experience: 7+ years of professional software development and platform/DevOps engineering experience with a track record of independently owning complex architecture end-to-end; a Bachelor's degree in CS or Software Engineering is preferred.
Production Python Mastery: Strong command of Python as a primary language, with a history of setting code quality standards, writing clean application logic, and building scalable API integrations.
Infrastructure as Code & Cloud Operations: Deep expertise in Terraform (modules, state management, remote backends) and hands-on operational mastery of AWS and Azure managed services.
Cloud Networking & Security Discipline: Robust understanding of cloud networking (VPCs, routing, security groups, private endpoints, TLS) alongside security practices (OAuth, SAML, Okta, secrets management, and RBAC).
Containerization & CI/CD Pipelines: Production experience designing and operating containerized workloads using Docker and Kubernetes, alongside owning automated CI/CD pipelines (Jenkins, Bitbucket, GitHub Actions).
RAG Infrastructure & AI/Agent Awareness: Practical knowledge of RAG pipelines (vector search, chunking, re-ranking), agent frameworks, tool-calling interfaces, and AI-specific security risks (e.g., prompt injection, Lethal Trifecta).
What Do We Value:
How We Work & What We Offer:
The base pay range for this position in Canada is $130k - $180k / year + bonus
Benefits and working arrangements may vary depending on your seniority, location and employment type. The compensation offered will be determined by factors such as relevant qualifications, experience, knowledge and skills. Many of our positions are eligible for additional types of compensation (e.g., commission plans, bonus, etc.) which our Talent Acquisition team will share with you if you interview for the role.