AI ML Architect / Senior FDE Lead

Anthrobyte•Prosper, TX

About The Position

We are looking for a True AI Forward Deployed Engineer (as coined by Palantir!) who can architect enterprise-grade AI systems, not just prototype them. We are not looking for someone to hand off a design doc and walk away. We are looking for someone to take AI all the way — from system architecture to production infrastructure that scales with the client’s business. This is a senior technical leadership role at the intersection of architecture rigor, hands-on engineering, and production-grade delivery. Anthrobyte builds production-grade enterprise AI systems for global clients across industries. As we build out our presence in the U.S. from San Francisco, we need a senior AI/ML architect who can hold the full technical journey: system architecture, agentic and LLM infrastructure design, hands-on build, and production deployment — all as one coherent capability. This is not a role where you hand off an architecture diagram and move on to the next project. You will work directly with the CTO, founding team, and enterprise clients to define how AI systems are architected, engineered, and hardened for production inside complex organisations. You will be the person who walks into a room with a messy, high-stakes technical problem and walks out with an architecture the team can actually build — and then builds it.

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

Responsibilities

Own the architecture. Ship it to production.
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