AI ML Architect / Senior FDE Lead

Zoho; Prosper, Northern (TX, KY); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Anthrobyte seeks a senior AI/ML architect and forward-deployed engineer to own the full technical journey from system design to scalable production infrastructure in Prosper, TX. You will work directly with executives and clients to define architectures that are practical, scalable, and secure.

The role emphasizes hands-on build, production readiness, and mentoring, with a path toward principal technical leadership and enterprise-scale deployments across industries.

Qualifications

• 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.
• Deep hands-on fluency: Python, distributed systems, LLM frameworks, and cloud infrastructure.

Responsibilities

• Own the architecture. Ship it to production.
• Forward deployment, production engineering, and system hardening for client engagements.
• Act as technical authority to clients and internal teams across architecture decisions.
• Define AI architecture patterns for LLM orchestration, RAG pipelines, and evaluation.
• Mentor engineers and contribute to reusable accelerators and tooling.

Skills

AI architecture

Production-grade delivery

Client engagement

Python

Distributed systems

LLM frameworks

Cloud infrastructure

LangChain

LlamaIndex

HuggingFace

Tools

LangChain

LlamaIndex

HuggingFace

AWS

Azure

GCP

Job description

Prosper, United States | Posted on 15/09/2026

Weare looking for a True AI Forward Deployed Engineer (as coined by Palantir!)who can architect enterprise‑grade AI systems, not just prototype them. Weare not looking for someone to hand off a design doc and walk away. We arelooking for someone to take AI all the way – from system architecture to productioninfrastructure that scales with the client’s business.

A senior technical leadershiprole at the intersection of architecture rigor, hands‑on engineering, andproduction‑grade delivery.

Anthrobyte builds production‑grade enterprise AI systems forglobal clients across industries. As we build out our presence in the U.S. fromSan Francisco, we need a senior AI/ML architect who can hold the full technicaljourney: system architecture, agentic and LLM infrastructure design, hands‑onbuild, and production deployment — all as one coherent capability.

This is not a role where you hand off an architecture diagramand move on to the next project. You will work directly with the CTO, foundingteam, and enterprise clients to define how AI systems are architected,engineered, and hardened for production inside complex organisations. You willbe the person who walks into a room with a messy, high‑stakes technical problemand walks out with an architecture the team can actually build — and thenbuilds it.

CURRENT ROLE

Ownthe full technical journey of AI engagements — architecture, hands‑on build,forward deployment, and production hardening — as one coherent capability.

Growinto executive‑level technical leadership — shaping the company’s AIarchitecture standards, technical IP, and engineering practice at scale.

Requirements

Own the architecture. Ship itto production.

• Lead architecture discovery with enterpriseclients — assess data maturity, system landscape, and AI readiness before asingle line of code is written
• Own end‑to‑end system design for AI proposals:model selection, RAG/agentic architecture, data pipeline design, infrastructuresizing, and ROI framing
• Build working prototypes, architectureblueprints, and technical proof points that de‑risk the engagement before fullbuild
• Set technical standards and reusablearchitecture patterns that the broader engineering team builds on

Forward Deployment & Production Engineering

• Be hands‑on in the build — write productioncode, design data pipelines, and stand up the infrastructure your architecturecalls for
• Own deployment realities: integrationcomplexity, security and compliance constraints, scaling, and observability
• Drive go‑live milestones, uptime, andpost‑deployment performance with direct accountability for outcomes
• Debug, iterate, and adapt in production — whensomething breaks, you own the fix, not just the postmortem

Technical Leadership & Client Engagement

• Act as the trusted technical authority to clientstakeholders — CTOs, VPs of Engineering, platform teams — not just a vendor onthe call
• Translate deep technical tradeoffs into languagebusiness stakeholders can act on, without losing the substance
• Collaborate with presales and delivery teams toensure every commitment made to a client is technically buildable
• Build long‑term technical trust with clientsthat turns single engagements into expanded, multi‑year mandates

Platform & Applied AI Research

• Define AI architecture patterns for the firm:LLM orchestration, RAG pipelines, agentic workflows, and evaluation frameworks
• Stay ahead of applied AI research and emergingframeworks; bring what matters back to the team before it’s common knowledge
• Mentor AI engineers on both architecture rigorand forward‑deployed delivery craft
• Contribute to Anthrobyte's technical knowledgecapital — architecture playbooks, reusable accelerators, and internal tooling

THE GROWTH PATHWAY

Demonstrate consistent excellenceand the scope expands. You will grow into principal technical leadership —shaping the company’s AI architecture strategy, representing engineering atinvestor and board conversations, building and leading a growing engineeringteam, and defining the technical standard for every client engagement. This is not a title — it is a level of ownership thatmust be earned and continually re‑earned.

You operate across the full stack — from distributed systemsdesign to client boardroom. You simplify complex architectures without losingwhat makes them robust, and you're as comfortable in a production incidentchannel as you are presenting system design to a VP of Engineering.

You Bring

• 8+ years of hands‑on AI/ML and softwarearchitecture experience, with production systems at real scale
• Proven track record architecting and shippingLLM-based or agentic AI systems into production — not just PoCs
• Deep hands‑on fluency: Python, distributedsystems, LLM frameworks (LangChain, LlamaIndex, HuggingFace), and cloudinfrastructure (AWS, Azure, GCP)
• Experience designing end‑to‑end AI/ML platforms:data pipelines, model serving, evaluation, and monitoring
• Strong client‑facing communication — able todefend architecture decisions to both engineers and executives
• Comfort operating in ambiguity and 0 → 1 environments; startup orforward‑deployed experience strongly preferred
• A bias toward ownership — you close loopswithout being asked, and you treat production issues as yours to fix