Machine learning Solution Architect: Remote Role
Role:Machine learning Solution Architect Location:Denver, CO or Remote Role Duration:6months Description:Technical breadth:Comfortable discussing modern distributed application architectures (APIs, services, identity/authn/authz, networking, observability, reliability), agent/LLM patterns (e.g., RAG, workflows/execution graphs), and integration into existing apps.Enterprise integration mindset:Integrates solutions with existing enterprise tools and services first (SSO, RBAC, secrets management, gateways, network paths, service accounts) to avoid one-offs and duplicative effort, while staying within security and architecture guidelines.Troubleshooting & incident leadership:Hypothesis-driven debugging, log/trace-based diagnosis, crisp escalation, and coordinated resolution across engineering/ops; able to turn fixes into reusable patterns and runbooks.Product thinking:Translates customer requirements and integration constraints into reusable, platform-level features with clear acceptance criteria 10years in solution architecture, forward-deployed/embedded engineering, platform engineering, delivery architecture, technical consulting, or SRE/DevOps supporting application teams.Hands-on experience building or extending shared platforms (AI/ML, data, or integration) and SaaS solutions, working through their constraints to deliver production outcomes.Proven track record working directly with customer/stakeholder teams to deliver solutions into production.Experience working closely with product owners to turn customer needs into platform features and roadmap items.Strong written and verbal communication with senior technical and business audiences; able to produce clear technical artifacts.Understands core ML concepts, basic stats, and A/B-style evaluation enough to collaborate with data science teams.Familiarity with challenges of LLM/agent/RAG-style solutions (variability, guardrails, evaluation) and how to drive robust, repeatable solutions.Comfortable in Python (or similar, e.g., TypeScript/Java) to use platform tooling, call APIs, build sample integrations, and debug issues.Nice-to-have:Background in AI/LLM/agentic systems or adjacent data/ML platforms; experience with enterprise identity/security patterns (SSO, OAuth/OIDC, RBAC) and network segmentation; history of creating reusable enablement assets and resolving complex escalations with senior stakeholders..