AI ML Architect / Senior FDE Lead

Anthrobyte•Prosper, TX

About The Position

Anthrobyte is seeking a highly experienced AI Forward Deployed Engineer (FDE) to architect and deliver enterprise-grade AI systems. This senior technical leadership role requires a blend of architectural rigor, hands-on engineering, and production-grade delivery. You will own the entire technical journey of AI engagements, from system architecture and agentic/LLM infrastructure design to hands-on build and production deployment. This is a unique opportunity to work directly with the CTO, founding team, and enterprise clients to define and implement cutting-edge AI solutions within complex organizations. The role involves assessing client needs, designing end-to-end AI systems, building prototypes, setting technical standards, and ensuring successful production deployment, including integration, security, scaling, and observability. You will act as a trusted technical authority, translating complex technical concepts for business stakeholders and collaborating with presales and delivery teams. Additionally, you will contribute to defining AI architecture patterns, researching applied AI, mentoring engineers, and building Anthrobyte's technical knowledge capital. The growth pathway leads to executive-level technical leadership, shaping the company's AI strategy and potentially leading engineering teams.

Requirements

8+ years of hands-on AI/ML and software architecture experience, with production systems at real scale
Proven track record architecting and shipping LLM-based or agentic AI systems into production — not just PoCs
Deep hands-on fluency: Python, distributed systems, LLM frameworks (LangChain, LlamaIndex, HuggingFace), and cloud infrastructure (AWS, Azure, GCP)
Experience designing end-to-end AI/ML platforms: data pipelines, model serving, evaluation, and monitoring
Strong client-facing communication — able to defend architecture decisions to both engineers and executives
Comfort operating in ambiguity and 0→1 environments; startup or forward-deployed experience strongly preferred
A bias toward ownership — you close loops without being asked, and you treat production issues as yours to fix

Nice To Haves

Startup or forward-deployed experience strongly preferred

Responsibilities

Lead architecture discovery with enterprise clients — assess data maturity, system landscape, and AI readiness before a single line of code is written
Own end-to-end system design for AI proposals: model selection, RAG/agentic architecture, data pipeline design, infrastructure sizing, and ROI framing
Build working prototypes, architecture blueprints, and technical proof points that de-risk the engagement before full build
Set technical standards and reusable architecture patterns that the broader engineering team builds on
Be hands-on in the build — write production code, design data pipelines, and stand up the infrastructure your architecture calls for
Own deployment realities: integration complexity, security and compliance constraints, scaling, and observability
Drive go-live milestones, uptime, and post-deployment performance with direct accountability for outcomes
Debug, iterate, and adapt in production — when something breaks, you own the fix, not just the postmortem
Act as the trusted technical authority to client stakeholders — CTOs, VPs of Engineering, platform teams — not just a vendor on the call
Translate deep technical tradeoffs into language business stakeholders can act on, without losing the substance
Collaborate with presales and delivery teams to ensure every commitment made to a client is technically buildable
Build long-term technical trust with clients that turns single engagements into expanded, multi-year mandates
Define AI architecture patterns for the firm: LLM orchestration, RAG pipelines, agentic workflows, and evaluation frameworks
Stay ahead of applied AI research and emerging frameworks; bring what matters back to the team before it’s common knowledge
Mentor AI engineers on both architecture rigor and forward-deployed delivery craft
Contribute to Anthrobyte's technical knowledge capital — architecture playbooks, reusable accelerators, and internal tooling