System Architect

microTECH Global LTD

London

1 year ago

Applications closed

Related Jobs

View all jobs

Lead MLOps Engineer

Software Engineer III- Data Engineer, Java/Python

HR Systems & Data Analyst

Senior Data Engineer - Hybrid Data Platform Lead

Data Engineer III - Python, Databricks & AWS

Lead Software Engineer - MLOps Platform

Native Intelligence System Architecture

Location - London (Permanent)

Salary - Competitive

Microtech-Global is looking for a Native Intelligent System Architecture Expert with experience in native intelligence construction of the HarmonyOS system. We are seeking to hire an expert who focuses on technologies related to terminal intelligence, including but not limited to the integration of LLM with OS, Agent architecture, user profiling, edge-cloud collaboration, foundational models for general artificial intelligence, multimodal large models, as well as model compression and optimization. Keep a close eye on industry trends and key technologies in the field of terminal intelligence, and incubate trends and technology roadmaps for software and hard intelligence transformation over 2-3 years.

Responsibilities:

Participate in the native intelligence construction of the HarmonyOS system, lead the architecture and development of terminal operating system components that can flexibly support AI capabilities, better serve AI capabilities, improve the efficiency of AI model operation, and build the architectural competitiveness of HarmonyOS in the field of AI.
Use machine learning capabilities to optimize underlying system performance, resource management, and power consumption to achieve optimal global efficiency and enhance the performance of HarmonyOS devices.
Participate in the construction of intelligent features of HarmonyOS applications, design flexible and scalable application frameworks that can better adapt to leading AI technology empowerment.
Introduce new technologies to the team, verify key technical points, grasp new technical directions in the industry, ensure that the architecture has good technical compatibility. Create detailed architectural documents, design specifications, and AI integration technical guidelines.

Required:

Familiar with operating system architecture, have a clear understanding and knowledge of the underlying system architecture, and have experience in applying intelligent technology at the system level. Deeply observe and analyse the intelligent direction of industry-leading products and formulate years AI native OS plan to promote the landing of AI-native OS.
Have in-depth academic research capabilities and achievements in the field of artificial intelligence (including natural language processing, computer vision, and decision-making reasoning). Familiar with Huawei's business, have rich experience in technological innovation and technical research projects, deeply observe and analyze the technical competitiveness and solutions of industry leading AI products.
Have a certain influence in the field of AI research, be able to quickly obtain industry technical resources, and transform them into internal competitiveness. Understand the commercial value behind the mainstream technology routes in the industry and their impact on the company's AI research strategy.

If you are interested, please apply below or send your CV to

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Jan 29, 2026

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Jan 24, 2026

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.