· Role: Forward Deployed AI Engineer
Type: Full-time position
Location: San Diego, CA - 100% Remote
Tech stack:
Python, TypeScript, AI tools (e.g., ChatGPT, Claude Code, Cursor)
About this role:
The Forward Deployed AI Engineer is a critical role at Cadre AI. You are the person in the room with the client, the person writing the code, and the person architecting the system. There is no handoff between “strategy” and “execution.” You own both. You will embed directly with our clients to understand their operations, identify the highest-leverage AI opportunities, and then build and ship production systems that deliver measurable results. One week you might be designing an LLM pipeline that processes financial documents. The next, you’re standing up a voice agent for a construction company or building a revenue operations engine for a hardware manufacturer scaling globally. This role is AI-first in how you work, not just what you build. You use Claude Code, Cursor, Codex, and whatever tools let you ship quality code at a pace that would be impossible without them. Speed and quality are not tradeoffs here. They’re both the expectation.
What you''ll do:
Own Client Delivery End-to-End
Embed with clients across private equity, lending, real estate, and SaaS to map their operations and identify the AI use cases that will move the business
Run discovery sessions, translate business pain into technical architecture, scope the work, and then build it yourself
Present to C-suite stakeholders, translating complex trade-offs into clear decisions with measurable expected outcomes
Manage client relationships as a trusted technical partner, not a vendor. Clients should feel like you’re part of their team
Build and Ship Production AI Systems:
Architect and deploy LLM/RAG pipelines, agent orchestration systems, conversational AI, document intelligence, and predictive analytics
Use AI-native development tools (Claude Code, Cursor, Codex) to write, review, and iterate on production code at startup speed
Build evaluation frameworks, regression suites, and observability layers that prove your systems work and catch when they don’t
Convert proofs-of-concept into stable, monitored production services. The demo is not the deliverable. The running system is
Design and enforce data quality rules, ETL pipelines, and secure cloud deployments across AWS, Google Cloud Platform, and Azure
Drive Strategy and Scale the Practice:
Contribute to Cadre’s 8-pillar AI transformation framework, turning field learnings into repeatable playbooks
Identify patterns across engagements and build reusable components, prompt libraries, and architecture templates that accelerate future pods
Scout emerging models, tools, and research (Claude, GPT, Gemini, Llama, open source). Run experiments and share what works with the team
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