Forward Deployed AI Engineer
Role details
Job location
Tech stack
Job description
- Develop and extend our Brand Knowledge Graph in Neo4j: modeling how changes to one node propagate and affect AI visibility and recommendations elsewhere
- Build predictive optimization systems that model how specific content and strategy changes impact AI visibility outcomes
- Design measurement and feedback loops that connect changes to outcomes and track predicted vs. actual lift over time
- Implement scoring logic including Share-of-Prompt, accuracy measurement, sentiment analysis, and competitor mention detection
- Analyze how optimizations at scale affect long-term LLM behavior and ecosystem dynamics, * Build and maintain integrations with LLM platforms including ChatGPT, Claude, Grok, Perplexity, DeepSeek, and others as they emerge
- Design orchestration systems that minimize unnecessary LLM calls: managing cost, latency, and quality tradeoffs intelligently
- Develop agent-based analysis workflows that evaluate multiple optimization scenarios in parallel and forecast impact
- Compare and stress-test multiple optimization strategies simultaneously to surface the most effective approaches
FULL-STACK PRODUCT ENGINEERING
- Build and ship full-stack features across frontend, backend, and data layers: from idea to production
- Develop high-quality frontend interfaces in React and TypeScript that translate complex graph and model outputs into actionable insights for users
- Design and optimize backend systems for performance, security, and scalability
- Build workflow and tracking infrastructure recording what changed, why it changed, and the outcome - integrating with Jira, Slack, HubSpot, and email
- Lay foundations for AI-native commerce experiences where merchants can transact directly inside AI chat
- Work closely with design and product to translate ideas into polished, production-ready experiences
CLIENT-FACING + QA
- Participate actively in client calls, demos, and technical conversations - explaining systems clearly to non-technical enterprise stakeholders
- Own QA processes and fix pack delivery: building test coverage, triaging bugs, and maintaining data integrity across the platform
- Pull insights from complex datasets and translate them into findings that clients and internal teams can act on
- Document architecture, decisions, and systems to support a growing team and future CTO onboarding, You get energy from variety. In a given week you might be extending the knowledge graph, debugging an LLM orchestration flow, presenting analysis to an enterprise client, and shipping a frontend improvement. You are not looking for a quiet corner of a large eng org. You want to be close to the work, close to the product, and close to the client.
Requirements
Do you have experience in TypeScript?, Equally important, you're comfortable operating autonomously and taking full ownership from concept through deployment. You can build it, explain it, demo it, and defend the technical decisions behind it, translating complex systems into clear narratives for clients without needing to be managed step-by-step., * 5+ years of software engineering experience in fast-moving, high-performance environments
- Strong full-stack engineering experience - you have shipped real products end-to-end, not just maintained existing ones
- Comfortable operating in client-facing settings. With an ability to clearly explain technical architecture, AI methodology, and product decisions to non-technical stakeholders with confidence.
- Deep hands-on experience with Neo4j and graph data modeling
- Practical experience integrating LLMs and building production-grade API integrations with AI platforms
- Ability to design systems that avoid constant LLM calls and manage cost, latency, and quality tradeoffs
- Proficient in React, TypeScript, Node.js, and modern frontend and backend frameworks
- Experience designing and working with scalable databases and APIs
- Clear, confident communicator who is energized by client interaction - not just tolerant of it. With the ability to lead technical demos, respond to live client questions, and translate complex systems into clear business value narratives.
Nice to Have
- Experience with applied data science, ML-adjacent systems, or experimentation frameworks
- Background in AI search, recommendation systems, SEO-like ranking models, or similar
- Practical agent orchestration experience - deterministic, evaluable, production flows
- Familiarity with conversational commerce or AI-powered transactional flows
- Experience working on creative or content-driven platforms
Who Thrives Here
You move fast and finish things. You are comfortable owning a feature from whiteboard to production, and you do not need someone to hand you a spec. You bring strong opinions about how to build things well and you are willing to defend them in a conversation, then document them afterward.