ML Architect
Posted 9 Oct 2026
Advertised as “Principal AI/ML Architect”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8+ yrs
- Management level
- People management
- Employment
- Remote
No salary listed for this role.
Field of studyComputer Science, Engineering (or related)
Study levelBachelor's or higher
Principal ML Architect (Client-Facing, Agentic AI) - Spain
Location: Spain (Fully Remote)
Build the Next Generation of AI Products with TensorOps
TensorOps is an applied-machine-learning studio that helps organisations across Europe and North America design, train, and deploy production-grade GenAI systems. Our team blends research depth with pragmatic engineering, and we’re looking for a seasoned architect to lead our client engagements and technical teams in Portugal.
What We’re Working On
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Conversational Copilots: Intelligent assistants that augment and empower knowledge workers.
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Autonomous Agentic Systems: Sophisticated agents that perform complex research, analysis, and actions for market-leading platforms.
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High-Performance Search & Recommendation: Building large-scale, low-latency recommender systems, ad-tech platforms, and search solutions.
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Decision-Support Tools: High-stakes systems for critical applications in healthcare, finance, and e-commerce.
Core Stack
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Python, FastAPI, Docker
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TensorFlow, LightGBM, CatBoost
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Open-source & Commercial LLMs
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LangChain / LangGraph, Langfuse, LlamaIndex
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MLFlow, Kubeflow
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AWS and GCP
The Role
As a Principal ML Architect, you will be the senior technical leader for our most ambitious client projects. You will act as a trusted advisor to clients, a mentor to your engineering team, and the chief architect for complex AI systems. This is a hands-on leadership role that combines deep technical expertise with strategic client management.
You will:
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Lead Technical Strategy & Architecture: Partner with clients to understand their core business challenges and design sophisticated, scalable solutions leveraging machine learning, search, and agentic AI paradigms.
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Manage Client Engagements: Serve as the primary technical point of contact for client stakeholders, translating complex concepts, managing expectations, and ensuring project success from a technical and business perspective.