Machine learning architect job description.
Looking for a machine learning architect or stepping into senior AI system design? This job description highlights responsibilities such as defining infrastructure, optimising model pipelines, and scaling ML operations. It includes skills in demand and senior pay expectations.
What does a machine learning architect do?
A machine learning architect designs the systems and infrastructure required to deploy, scale, and monitor machine learning solutions. They bridge data engineering, software development, and AI modelling.
Their responsibilities include defining data pipelines, model serving architecture, MLOps strategy, and monitoring frameworks. They work closely with ML engineers, Data architects, and DevOps to ensure models move from experimentation to production efficiently.
In startups, they may oversee the entire AI lifecycle. In large enterprises, they lead architecture for platforms that support multiple models and stakeholders, ensuring governance, scalability, and performance.
Key responsibilities of a machine learning architect.
ML architects design systems and infrastructure to support scalable machine learning. Responsibilities typically include:
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Designing architecture for training, deployment, and monitoring pipelines
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Defining infrastructure standards for ML development and MLOps
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Evaluating cloud-native ML tooling and infrastructure components
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Collaborating with security and compliance on AI risk frameworks
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Ensuring reproducibility, governance, and lifecycle tracking
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Working with data engineers to support feature stores and streaming pipelines
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Supporting platform scaling, model registry, and resource provisioning
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Leading architectural reviews and platform planning with engineering
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Documenting system design, dependencies, and trade-offs
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Advising leadership on AI infrastructure roadmap and investments
This role blends system design, ML engineering, and enterprise architecture.
Skills and requirements for a machine learning architect.
Machine learning architects design systems and infrastructure supporting scalable AI. Employers typically look for:
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8–12 years of experience in ML, data engineering, or systems architecture
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Proven experience deploying ML pipelines and model management platforms
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Deep knowledge of cloud services, containerisation, and distributed systems
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Experience with model lifecycle design, testing, and versioning
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Familiarity with data governance, ethics, and AI compliance
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Ability to integrate third-party tools and APIs
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Skilled in cross-functional collaboration with data, engineering, and product teams
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Strong understanding of infrastructure cost, monitoring, and optimisation
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Comfortable setting standards for experimentation and production readiness
Most ML architects guide AI strategy, ensuring accurate, scalable systems.
Average salary for a machine learning architect.
In the UK, the average salary for a machine learning architect typically ranges from £70,000 to £110,000, based on ML system architecture, scaling strategy, and cloud deployments.
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Mid-level ML architects typically earn between £70,000 and £90,000
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Senior architects with enterprise or multi-product responsibility may earn between £91,000 and £110,000
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Strong experience in MLOps, data governance, and orchestration drives top compensation
Roles in AI platforms, cloud consultancies, and digital transformation programs offer the highest pay.
Career progression for a machine learning architect.
A machine learning architect designs scalable, secure, and high-performing ML systems. This is a senior technical leadership role, often with a hybrid focus across engineering, infrastructure, and data science. A typical journey includes:
ML engineer / Data engineer
Builds training pipelines, model serving layers, and monitors performance metrics.
Senior ML engineer / MLOps specialist
Leads deployment, builds feature stores, and ensures model governance.
ML architect
Designs architecture for real-time, batch, or hybrid ML systems. Integrates with cloud and data platforms.
Director of ML / Platform lead
Owns system-wide scalability, infrastructure investment, and roadmap planning.
CTO / Head of AI
Drives long-term ML strategy across products, platforms, and organisational functions.
salary guide
Our UK data salary guide.
ML architects design the systems and infrastructure to support model training and deployment. Offers should reflect depth in architecture, tooling, and governance.
Use our UK data salary guide to benchmark ML architect salaries, compare year-on-year data from 2024, and plan confidently for 2026.
FAQS
Machine learning architect FAQs.
They design the end-to-end system for ML at scale — deciding on model frameworks, deployment infrastructure, feature stores, monitoring, and governance. It’s a systems-level role that spans experimentation, ops, and compliance.
Experience scaling from prototype to production, defining reusability standards, and solving governance challenges. Look for candidates who can talk about lineage, model reproducibility, and hybrid cloud infrastructure trade-offs.
Large banks, insurance, ecommerce platforms, and healthtech providers. Anywhere ML is mission-critical and requires long-term platform investment — not just experimentation.
Progression to head of ML, AI infrastructure director, or CTO-level roles — especially in AI-first or platform-focused businesses.
