Machine learning Solution Architect: Remote Role

<p><b>Role: Machine learning Solution Architect</b></p> <p><b>Location: Denver, CO or Remote Role</b></p> <p><b>Duration: 6+ months</b></p> <p> </p> <p><b>Description:</b></p> <ul> <li>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. </li> <li>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. </li> <li>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. </li> <li>Product thinking: Translates customer requirements and integration constraints into reusable, platform-level features with clear acceptance criteria </li> <li>10+ years in solution architecture, forward-deployed/embedded engineering, platform engineering, delivery architecture, technical consulting, or SRE/DevOps supporting application teams. </li> <li>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. </li> <li>Experience working closely with product owners to turn customer needs into platform features and roadmap items. </li> <li>Strong written and verbal communication with senior technical and business audiences; able to produce clear technical artifacts. </li> <li>Understands core ML concepts, basic stats, and A/B-style evaluation enough to collaborate with data science teams. </li> <li>Familiarity with challenges of LLM/agent/RAG-style solutions (variability, guardrails, evaluation) and how to drive robust, repeatable solutions. </li> <li>Comfortable in Python (or similar, e.g., TypeScript/Java) to use platform tooling, call APIs, build sample integrations, and debug issues. </li> </ul> <p style="margin-left:48px"> </p> <p><b>Nice-to-have:</b> </p> <ul> <li>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. </li> </ul>