About Climb

Climb is a Data and AI consultancy that partners with enterprises to design, build, and operationalize modern data platforms and production AI systems. As a Databricks partner, we go deep on lakehouse architecture, machine learning, and applied AI, with a bias toward production over proof of concept. Our team brings deep technical expertise and a builder's mindset to every engagement, and we measure our work not just by what ships, but by the business impact it drives.

Role Summary

The Forward Deployed Architect owns the technical architecture of a client engagement. You take business objectives and translate them into a production-ready system, defining the platform architecture, technical standards, integration patterns, and implementation approach that guide delivery from discovery through production.

You design and build complete software systems: front-end, back-end, and the infrastructure and CI/CD that ship them. That includes applied AI, which is software built around non-deterministic models, where you design for evaluation, fallbacks, and failure handling the same way you design for any other part of the system. You make the right calls on synchronous and asynchronous patterns and build systems that are scalable, reliable, and operable. This is not a data engineering or data architecture role.

This is a hands-on role. You stay close to the implementation, solving the hardest technical problems, reviewing critical design decisions, and writing code where it adds the most value. You keep the system coherent as multiple workstreams come together, balancing scalability, security, maintainability, performance, cost, and delivery speed.

Key Responsibilities

Own the end-to-end system architecture on your engagement: front-end and back-end architecture, service boundaries, integration patterns, sync and async design, security, observability, 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 (frameworks, services, infrastructure, model selection where applicable, data flow, security, and cost) and stand behind them.

Own the path to production: CI/CD, infrastructure as code, environments, and the reliability of what ships.

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.

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.

Required Qualifications

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 full-stack depth: front-end with modern frameworks (React, Next.js, or similar) and back-end services in languages such as TypeScript, Python, Go, Java, or C#.

Experience designing systems that combine synchronous and asynchronous patterns (APIs, queues, event-driven processing, background workers, durable workflows), with a clear grasp of the tradeoffs in latency, reliability, consistency, and cost.

Hands-on with CI/CD, infrastructure as code (Terraform, Pulumi, CDK, or similar), and containers.

Build for operability: observability (logs, metrics, traces), SLOs, idempotency and retries, and safe rollout and rollback.

Experience designing around non-deterministic components, such as ML models, LLMs, or unreliable third-party APIs, including evaluation, fallbacks, and failure handling.

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.