AI ML Architect / Senior FDE Lead

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.

01 ·  THE OPPORTUNITY

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.

CURRENT ROLE

AI ML Architect & Senior FDE Lead

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

GROWTH TRACK · MERIT-BASED

Principal AI Architect

Grow into executive-level technical leadership — shaping the company’s AI architecture standards, technical IP, and engineering practice at scale.

Requirements

02 ·  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

THE GROWTH PATHWAY

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

03 ·  WHO YOU ARE

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

·    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