climb-ai - Forward Deployed Architect
Requirements
• 8+ years of software engineering or technical architecture experience, including production systems in cloud environments (AWS, Azure, or GCP). • Proven experience serving as the technical design authority for production systems delivered to customers. • Hands-on depth across one or more of: LLM application and agent development, production data engineering, or backend systems at scale. • Demonstrated ability to guide multiple engineers while maintaining architectural consistency across the system. • Still codes: comfortable working on the critical path and proving out the hard parts yourself. • Strong communicator: technically precise with engineers, clear and confident with VP- and C-level leadership. • High tolerance for ambiguity; can turn a loosely scoped problem into a build the team can execute. • Experience building production AI systems: RAG pipelines, agents, fine-tuned models, or LLM-native applications. • Hands-on experience with Databricks (Delta Lake, Workflows, Unity Catalog, Mosaic AI) is a strong plus. • Background in a consulting firm, systems integrator, or professional services environment. • Familiarity with SOW-based, outcome-priced delivery and active scope management. • Note on certification: Databricks experience is valued but not required at hire. Both Databricks and Anthropic certification are expected to be obtained post-hire. • You think in whole systems, not just the component in front of you, and you keep the architecture coherent as the build grows. • You stay close to the code; you'd rather prove the hard part yourself than hand it off and hope. • You can hold the technical line with a client's architects and still get into the details with the engineers building it.
Responsibilities
• Own the end-to-end system architecture on your engagement: platform architecture, data and AI design, integration patterns, observability, security, and operational readiness. • Take engagement objectives and turn it into a coherent target architecture and an implementation plan the engineering team can execute against. • Make the platform and pattern decisions, model selection, agent and pipeline architecture, data flow, security, and cost, and stand behind them. • Guide and review the engineering team's implementation, ensuring individual workstreams integrate into a coherent production system. • Stay hands-on: write code on the critical path and prove out the hard parts of the architecture yourself. • Partner with the Forward Deployed AI Lead to align architecture with business priorities, engagement scope, and delivery sequencing. • Deliver architecture documentation, implementation guidance, and production-ready systems that customers can confidently operate after handoff. • Contribute reference architectures, patterns, and accelerators back to the practice.
Benefits
• Ground floor, real backing. You are joining early, with founders who have built and exited firms like this before. You help write the playbook rather than inherit one. • Outcomes, not hours. We sell and deliver against business results. Advancement is tied to delivery performance and account impact, not utilization targets. • Senior team, no body-shop drag. Small pods of A-players, heavy internal AI leverage, and no bloated middle layers between you and the work. • IP that compounds. Every engagement feeds reusable accelerators, patterns, and points of view back into the practice. • Competitive base salary with performance-based bonuses • MacBook Pro and swag kit so you can do your best work • Comprehensive health, dental, and vision insurance • Generous holidays, flexible PTO, and remote-first work environment • Professional development budget including Databricks and cloud certifications • Spot bonuses for relevant certifications • Conference attendance and thought leadership opportunities • Collaborative, low-ego culture with direct access to leadership • Opportunity to shape a growing practice from the ground floor
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