Principal Forward Deployment Engineer (FDE)

Accellor•Seattle, WA

2d•$200,000 - $225,000•Hybrid

About The Position

Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value. Our offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability. With a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation. About the role: As a Principal FDE, you’ll be the senior technical leader inside our most strategic enterprise engagements. You'll embed with customers to translate AI ambitions into production architectures, designing how data, AI, and governance fit together end-to-end. You'll be the technical voice the customer trusts and the field-level expert whose patterns shape what we build next in product. You’ll lead FDE through high-stakes, ambiguous customer deployments and own technical and business value outcomes end to end. You’ll grow a team that can operate under pressure and help our organization learn from the field. The FDE works directly with strategic customers and designs how AI, data and governance come together end-to-end to ensure deployments are scalable, secure, and deliver measurable outcomes. This role goes beyond solution design. By shaping architectures, defining best practices, and identifying product gaps in real time, the Architect directly influences the evolution of the platform. Their work creates patterns that accelerate future deployments, strengthen technical credibility with enterprise buyers, and reduce time to value across engagements. You’ll partner closely with Product, Research, Sales, and GTM to ensure fieldwork informs roadmap priorities, drives new exploration, and supports safe deployment at scale. Your decisions will influence how we are trusted by the customers closest to our deployment work. Your success will be measured by how consistently your team ships, how clearly you deliver signal to Research and Product, and how durable your team and delivery model prove to be.

Requirements

15+ years of engineering or technical delivery experience, including 2+ years managing high-performing FDE or customer-facing engineers
7+ years as a Solutions Architect, Principal SE, Forward Deployed Engineer, or Technical Lead at a data platform, AI, or enterprise SaaS company
Customer-facing track record with senior technical buyers and architecture review boards
High agency; comfortable being the senior technical voice in the room with the customer
Ability to translate complex AI + data concepts into executive-ready architecture proposals
Has built and shipped production AI applications, not just prototypes
Worked on a SaaS Platform in an Architect Profile (or closely aligned role)
Have led high-pressure technical projects from prototype to production
Write and review production-grade code across frontend and backend using JavaScript or Python
Have built or deployed systems powered by LLMs or generative models and understand how model behavior affects product experience
Simplify complex work and make fast, sound decisions under pressure
Elevate team performance through clarity, not process
Operate with urgency in ambiguous or evolving environments
Translate field experience into sharp, actionable feedback for Product and Research
Build deep trust with your team by modeling calm, focus, and judgment when it matters most
Strong AI/ML literacy: LLM capabilities, agentic architectures, RAG patterns, prompt engineering, and when to apply each
Able to define Multi-tenant Architectural patterns and security objectives
Hands-on enterprise data integration: SQL, ETL/CDC pipelines, API design, ODBC/JDBC, and multi-source connectivity
Able to design governed data access for AI agents, RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements
Experience with modern data stacks (Snowflake, Databricks, Salesforce) and cloud-native deployment patterns
Experience mentoring junior engineers without requiring direct reporting relationships

Nice To Haves

Direct experience with the MCP protocol and AI agent frameworks (LangChain, CrewAI, Copilot Studio)
Prior experience as an FDE or in a similar embedded customer-facing engineering role

Responsibilities

Run technical discovery workshops with customer architects, data leaders, and AI teams, mapping data sources, MCP Workspace scoping, and agent tooling requirements
Own the scoping for AI deployments with clear acceptance criteria around agent accuracy, data coverage, and governance
Translate complex AI + data concepts into executive-ready architecture proposals; defend trade-offs to CxO-level stakeholders
Lead and grow a team of FDE delivering production systems with frontier models
Own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality
Own the technical solution end to end, from customer discovery and workflow scoping through architecture, hands-on implementation, evaluation, production deployment, adoption, and handoff
Partner credibly with customer engineers, operators, and domain experts to frame ambiguous problems, define scope, and translate business workflows into technical requirements and measurable outcomes.
Design solutions across various AI sources covering MCP server configuration, semantic context modeling, and governance integration
Architect agent orchestration, defining which data, schemas, and actions each agent can access in production
Design governed access patterns for AI agents: RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements
Define AI Best practices using agents, skills and LLMs to drive successful customer outcomes
Identify architectural and product gaps during live enterprise engagements and partner with Product and Engineering to define scalable solutions
Author technical specifications and implementation recommendations for enhancements, including both features and core architectural improvements
Build reusable reference architectures, deployment patterns, and MCP blueprints that reduce implementation friction and accelerate future customer deployments
Translate recurring customer deployment challenges into scalable platform capabilities and architectural standards
Codify what works into tools, playbooks, and roadmap inputs that create leverage for our enterprise customers
Notice early indicators and raise them with urgency, whether in product behavior, customer environments, or delivery practices
Use judgement to distinguish what requires action and what does not
Set a high bar for FDE performance and support each person’s growth through direct, actionable feedback
Define how we staff and support field teams that can scale without added complexity