SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: CAD 238,000 - 340,000 / annual
Docker is building a Professional Services organization to accelerate customer success and drive enterprise adoption of agentic workloads. As Principal Solutions Architect, you will play a critical role in building and scaling this function, working directly with senior leadership across revenue, product, marketing, and partner organizations.
You will shape service strategy, drive cross-functional alignment, and deliver offerings that accelerate both services revenue and platform expansion. This is a high-visibility role serving as a trusted advisor to Docker's largest and most strategic partners and enterprise customers, providing thought leadership on integrating Docker agentic solutions within enterprise environments.
Key responsibilities include:
**Build and Scale the Professional Services Practice:**
- Develop and codify repeatable reference architecture patterns across Docker's core use cases, including self-hosted and air-gapped deployments, MCP (model context protocol)/AI agent environments, IDP integrations, and secure supply chain workflows.
- Translate reference architectures into customer-facing technical collateral, SOW templates, and success patterns that reduce scoping time and improve delivery consistency.
- Collaborate with product, marketing, and revenue teams to integrate services into GTM motions and align delivery to Docker's AI platform roadmap.
- Identify offerings that drive product expansion, renewals, and long-term customer value.
- Support Partnerships & Alliances in selecting, onboarding, and enabling technical integration partners, system integrators, and service delivery partners.
**Drive Strategic Customer Outcomes:**
- Act as a trusted advisor to enterprise customers, helping them adopt modern patterns for containerization, AI workflows, and secure software supply chains.
- Lead technical discovery and architecture engagements spanning enterprise integration layers.
- Architect and deliver self-hosted Docker deployments within air-gapped, regulated, and complex enterprise environments, including local image registry setup, offline toolchain configuration, and compliance-driven infrastructure design.
- Design and guide customers through implementation of agentic AI toolchains, including MCP server/client architecture, tool orchestration, observability integration, and secure deployment patterns.
- Position Docker as a strategic and thought leadership partner, influencing long-term technology adoption and investment decisions.
- Directly deliver or assign and oversee appropriate delivery partners to execute service engagements and ensure technical objectives are met.
**Enable Cross-Functional Success:**
- Drive alignment and decision-making across multiple stakeholders and teams without direct reporting lines.
- Translate field insights—including enterprise integration patterns, IDP/SSO friction points, observability gaps, and AI deployment challenges—into actionable product and services strategy, roadmap, and partner capabilities.
**First 30 Days:** Onboard to Docker's mission and values; familiarize yourself with product portfolio, AI platform roadmap, and strategic customers; build partnerships with Product, Engineering, and Sales; plug into team rhythm.
**First 90 Days:** Scope and define SOWs for customer service engagements; participate in creation of reference architectures for key use cases; participate in initiatives to build out services offerings.
**One-Year Outlook:** Successfully deliver and oversee customer service engagements; contribute materially to build-out of Docker's service offerings across AI, security, and enterprise integration practices; institutionalize continuous improvement by providing field feedback to the product team.
**Requirements:**
- 7+ years as a Professional Services Solution Architect, Technical Consultant, or similar role at a SaaS/infrastructure company or consulting firm.
- Demonstrated experience architecting and deploying self-hosted and air-gapped deployment architectures: local image registries, offline toolchain configuration, network-isolated Kubernetes clusters, and compliance-driven infrastructure constraints common in regulated industries.
- Enterprise integration experience across Identity Providers (Okta, Azure AD, LDAP/Active Directory) and federated identity protocols (OIDC, SAML, SSO), including RBAC design within complex, multi-tenant enterprise environments.
- Working knowledge of Model Context Protocol (MCP) and its application to agentic AI toolchains, including MCP server/client architecture, tool and resource orchestration, and secure containerized deployment patterns.
- Hands-on experience with AI/ML workflows, LLM deployment, and agentic AI architectures in enterprise environments, with awareness of Docker's AI platform roadmap including AI agent containerization, model provenance, and AI supply chain security.
- Deep understanding of container and software supply chain security: Docker Hardened Images (DHI), Secure Supply Chain (SSC), SBOM generation, image signing and attestation, vulnerability management, and runtime governance.
- Excellent facilitation and stakeholder management skills; able to lead executive-level architecture conversations and influence senior stakeholders without formal authority.
- Direct experience delivering high-quality services engagements that achieve customer value while meeting revenue and ARR growth targets within a revenue organization.
- Recognition by enterprise customers as a strategic technical advisor.
**Bonus Qualifications:**
- Experience architecting AI platforms in large enterprise environments (LLM deployment pipelines, model provenance, artifact signing, and secure AI supply chain patterns).
- Background enabling system integrators, consultancies, or partner ecosystems.
- Consulting experience with Fortune 500 clients in highly regulated sectors such as financial services, healthcare, or government.
- Led AI security architecture engagements that addressed model provenance, artifact signing, and supply chain integrity for containerized AI workloads, including secure MCP server deployment, tool and resource access controls, secrets management within agentic pipelines, and runtime isolation patterns for LLM-based applications.
