Forward Deployed Engineer

Box•New York, NY

7h•$150,000 - $200,000•Hybrid

About The Position

Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia. By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.

Requirements

7+ years of development, consulting, professional services, or sales engineering experience.
Expertise in architecture / system design for cloud solutions (i.e. APIs, DevOps, Infrastructure, middleware, scale limits, rate limits, etc.).
Expertise in Enterprise Content Management systems and cloud migrations.
Deep understanding of prompt engineering, prompt governance, evaluation and testing frameworks, model selection, benchmarking, and security/compliance scoping for AI.
Proven experience designing and building production AI workflows or agentic systems — not just proof-of-concept
Hands-on proficiency with AI APIs and at least one major model ecosystem (OpenAI, Anthropic, Google, or equivalent); able to evaluate, confidence score, and select models by capability, cost, and task fit
Strong programming skills — Python and/or JavaScript — with experience building scripts, API integrations, and automation pipelines that move through a customer SDLC
Experience in post-sale professional services or consulting environments; comfortable owning customer relationships and driving outcomes over multi-month engagements
Experience with agentic frameworks and orchestration (LangChain, LlamaIndex, AutoGen, or similar)
Familiarity with retrieval-augmented generation (RAG), context pipeline design, and prompt engineering at production scale
Understanding of enterprise content governance, information architecture, and data security considerations in AI deployments
Experience with LLM evaluation methodologies — building evals, confidence scoring, human-in-the-loop review workflows
Experience working alongside model providers' forward-deployed teams in a complementary, context-layer capacity
You are a collaborative partner who is able and excited to work alongside our customer and their preferred partners to deliver best-in-class solutions

Nice To Haves

You are energized by the pace of AI — you follow model releases, emerging agentic patterns, and evolving best practices because you're genuinely curious, not because it's required
You think in systems and outcomes, not just features — you care whether AI makes a real difference to a business process, not just whether a model can generate an answer
You are comfortable translating complex technical concepts for executives and business stakeholders, and equally at home in a code editor or a whiteboard session
You bring consulting discipline to technical work — you formalize requirements, track deliverables, and hold yourself accountable to measurable customer value
You thrive in ambiguity and fast-moving environments — Box FDEs are often working at the frontier of what's possible, and the best ones lean into that
You are a trusted partner to customers, not a just a vendor — you understand their business deeply and advocate for their outcomes inside Box
You love working in a dynamic environment where ambiguity is common...and can effectively speak to both technical and non-technical parties.

Responsibilities

Assess and structure customers' content environments — information architecture, metadata, permissions, and governance — so models and agents can retrieve the most relevant, secure, and current content
Design relationship hierarchies, metadata libraries with associated templates, Hubs, integrations and permissions for agentic use cases
Run AI readiness assessments and deliver actionable roadmaps for customers preparing for Box AI-driven workflows
Partner with customer AI leadership to define holistic AI strategy, set priorities, and deliver prescriptive roadmaps — reporting progress to executives on an ongoing basis
Lead requirements workshops to formalize scope into achievable, tracked deliverables
Build production-quality code — scripts, apps, API integrations — that scales correctly and handles failures gracefully
Design and build end-to-end agentic workflows directly in our customer’s environments: context pipelines, prompts, generative steps, agents, and downstream integrations, including Box MCP server and customer-chosen orchestrators
Run programmatic model testing across approved models to find best fit per use case; build dashboards tracking reliability, speed, accuracy, and cost
Design every solution with AI unit and token efficiency at the center — forecasting spend and supporting ROI calculations
Own post-deployment reliability: monitoring, alerting, fallback paths, drift detection, and change-control so solutions stay accurate and trusted over time
Tune workflows as prompts drift, agents multiply, and models change — refining for cost and output quality
Drive end-user adoption through enablement, roadshows, and LOB-specific configuration
Document product gaps and work with Box Product & Engineering to influence roadmap priorities
Track KPI attainment and adoption signals post-launch; package learnings into repeatable patterns that scale across customers
Evolve deployment strategy with Customer as AI approaches and models advance