Forward Deployed AI Engineer

Go Cadre • San Diego, CA

9h • Onsite

About The Position

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.

Requirements

• 5+ years building software, with 3+ years focused on AI/ML systems in production (not notebooks, not proofs-of-concept)
• You have shipped LLM-powered products to real users and understand the gap between a working demo and a reliable system
• Hands-on with modern LLMs, RAG architectures, agent frameworks, and prompt engineering. You have opinions about when to use each and you can defend them
• Fluent in Typescript & Python and comfortable across the stack (React/Next.js, PostgreSQL, cloud infrastructure). You can build a full application, not just a model
• Exceptional communicator who can whiteboard architecture with engineers in the morning and present ROI to a CFO in the afternoon
• AI-native in how you work. You actively use AI coding tools to multiply your output and you’re always testing new ones
• Comfortable with ambiguity. You can walk into a client engagement with a vague problem statement and leave with a concrete plan
• Strong judgment about when to use prompt engineering vs. RAG vs. fine-tuning vs. classical ML based on the actual constraints (data, latency, budget, timeline)

Nice To Haves

• Former founder, early startup engineer, or someone who has operated like one inside a larger org. You know what it means to own the entire problem
• Track record of deploying AI systems that moved a business KPI, not just a model metric. You can point to the revenue gained, cost saved, or time eliminated
• Experience in consulting, professional services, or client-facing engineering where you had to earn trust and deliver under pressure
• You’ve led end-to-end launches of agent-based or LLM systems at production scale, including the unglamorous work of error analysis, edge case handling, and monitoring
• Active in the AI community. You write, speak, contribute to open source, or build in public. You’re a practitioner, not a spectator
• Domain experience in financial services, real estate, lending, or B2B SaaS

Responsibilities

• 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, GCP, 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
• Mentor junior engineers through pair programming, design reviews, and hands-on coaching

Benefits

• Real ownership. You will not be writing tickets for someone else to build. You scope it, build it, ship it, and see the impact with your own eyes
• Variety and velocity. Every engagement is a different industry, a different problem, and a new chance to build something that matters. You won’t get bored
• AI-native culture. We don’t just build AI for clients. We use it to run our own operations. You’ll work with people who are as obsessed with the tools as you are
• Access to the frontier. Through our partnerships with Anthropic, OpenAI, and YC, you’ll be among the first to experiment with new models and capabilities
• Upside. We’re a bootstrapped, profitable, fast-growing company. Early team members share in the success they help create
• No bureaucracy. Small pods. Clear accountability. The best idea wins, regardless of who says it

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed