Forward Deployed Engineer - Applied AI

Role overview A Forward Deployed Engineer sits at the intersection of applied AI engineering and customer delivery, embedding with client teams to translate business workflows into working agent-driven solutions. The role blends roughly 70–80% hands-on coding and integration with around 30% direct customer engagement, partnering closely with pre-sales, post-sales, engineering, and product teams to ship production AI agents, integrations, and automations on a unified AI-ready platform.

Responsibilities - Design, build, and launch AI agents, integrations, and automations that connect a core AI platform with customers' existing technology stacks and operational workflows. - Integrate SaaS and non-SaaS systems via APIs, webhooks, and real-time or event-driven architectures, ensuring reliable bidirectional data synchronization. - Apply prompt engineering, semantic search tuning, RAG patterns, and function calling to improve agent accuracy, then build evaluation harnesses to validate those gains. - Write SQL queries, analyze customer data, and surface insights through dashboards that inform product and business decisions. - Prototype quickly, run live technical demos, and iterate on solutions based on stakeholder and end-user feedback. - Coordinate continuously across engineering, product, customer success, support, and revenue teams to keep implementations aligned, with willingness to travel up to 30% for on-site workshops and go-lives.

Requirements - Five or more years of experience in software development, systems integration, or platform engineering, with comfort working directly with customers. - Strong proficiency in TypeScript, JavaScript, and Python, grounded in solid data structures and algorithms; familiarity with Go is a plus. - Hands-on applied AI experience with large language models, prompt engineering, retrieval-augmented generation, function calling, and evaluation design. - Deep familiarity with API integration patterns (REST, GraphQL, webhooks), large-scale one-way and two-way data synchronization, and pub/sub or event-driven architectures. - Experience deploying on serverless and edge platforms such as AWS Lambda and Google Cloud Functions, alongside modern DevOps practices including CI/CD, containers, and observability. - Skill in data mapping, schema alignment, heterogeneous system integration, and an understanding of data modeling and graph data structures. - Strong written and verbal communication, a customer-empathetic mindset, and a bachelor's or master's degree in computer science, engineering, or a related discipline.

Nice to have - Experience building context-aware integrations using Model Context Programming. - Background with code repository integrations and advanced configuration workflows. - Advanced degrees or certifications in AI or architecture frameworks such as TOGAF or SAFe.

Benefits and work setup - Travel expected up to 30% for on-site implementations, technical workshops, and customer engagements. - Equal opportunity employer policy emphasizing non-discrimination across protected categories.