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.
In this role you will:- 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
RequirementsYou might thrive in this role if you:- Bring 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 Have: - 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
BenefitsAt Accellor, we believe in equitable compensation. The base salary for this position will be in the range of $200,000 to $225,000 with additional revenue-linked performance incentives. The actual pay will depend on your skills, experience, and qualifications. The salary range is subject to change.
In addition, we offer:
Work-Life Balance: Accellor prioritizes work-life balance, which is why we offer, flexible work schedules, opportunities to work from home, and paid time off and holidays.
Financial and Medical Benefits: Our package includes perks like flexible and discretionary time off, healthcare coverage for you and your loved ones, and a retirement plan to help you plan for the future. Additionally, we offer access to flexible spending and health savings accounts, life and AD&D insurances.
Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training, Stress Management program, professional certifications, and technical and soft skill trainings.
Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them.
Collaborative Environment: You can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment - or even abroad in one of our global centres.
