Software Engineer
As a ML Architecht, you set the technical direction for our most complex Generative AI work — and you are personally hands-on building it.
About the opportunity:
As a ML Architecht, you set the technical direction for our most complex Generative AI work — and you are personally hands-on building it. We treat AI as a software engineering discipline, and you are the person who raises that bar: architecting systems that survive contact with production, and elevating the engineers around you to do the same.
You own the hardest problems in our portfolio — agentic platforms operating at scale, applied-ML work, and AI in regulated, high-stakes environments where being wrong is expensive. Document intelligence and RAG pipelines are part of the toolkit, not the ceiling. You take these systems from an ambiguous business problem to a robust, scalable production service, solving for the real-world constraints — latency, reliability, cost, governance — that separate a demo from a system a client can bet on.
You are also the technical face of our engagements. You partner directly with client leadership to translate business strategy into AI architecture, shape solutions in pre-sales, and earn the trust that turns a project into a program. This role blends deep, current mastery of LLMs and ML with the judgment, communication, and consultative instinct of a senior technical leader.
What You Will Do:
Set technical direction: Own architecture for our most complex GenAI and agentic systems end-to-end, and set the standards — evaluation, observability, responsible AI — that the practice builds to.
Build at the frontier, hands-on: Stay in the code where it matters most — foundation-model and embedding fine-tuning, novel agentic workflows, advanced RAG and semantic search — using Python on Google Cloud (Vertex AI), LangChain/LlamaIndex, and vector search (Vertex AI Vector Search, Pinecone, pgvector).
Engineer for production: Design for latency, reliability, cost, and scale from day one; apply MLOps discipline so systems are served efficiently, monitored, and continuously improved — and actually reach production, where most AI work stalls.
Lead multi-step reasoning at scale: Architect and operate agentic workflows that automate complex reasoning reliably, with the design and verification discipline that keeps multi-agent systems from cascading into failure.
Advise clients and shape deals: Work directly with client leadership to understand strategy, propose state-of-the-art approaches, and shape solutions in pre-sales — the technical authority in the room.
Multiply the team: Elevate senior and mid-level engineers through architecture reviews, mentorship, and setting a high, teachable bar for AI-augmented engineering.
Your Technical Toolkit:
Posted July 24, 2026