AI Architect - Global R&D Technology

IFS
Staines-upon-Thames
1 week ago
Applications closed

Related Jobs

View all jobs

Data & AI Architect, Microsoft Azure, PaaS, ETL, Data Modelling Remote

Solutions AI Architect (Pre-Sales) (Expression of Interest)

Principal AI Architect

Data & AI Architect

Principal AI Architect

AI Cloud Data Architect

Job Description

Are you ready to make waves in the world of machine learning and AI? We're on the hunt for an AI Architect to join our dynamic global R&D organization. We're looking for someone who brings the heat, fosters seamless collaboration, and is always chasing that next level of excellence.

You'll be at the forefront of infusing cutting-edge AI into IFS Cloud, revolutionizing Enterprise Resource Planning, Asset Management, and Field Service Management. Get ready to tackle high-stakes challenges like IIoT, predictive maintenance, forecasting, anomaly detection, optimization, and unleashing generative AI. Your machine learning and software engineer wizardry will power our solutions, crafting efficient and scalable services, expanding our AI infrastructure, and pushing the envelope of innovation across our product lineup.

Your sharp critical thinking and knack for real-world business dilemmas will be instrumental in orchestrating end-to-end solutions. From spotting opportunities on the horizon to delivering high-performance, scalable solutions, you'll play a pivotal role in our success.

If you're a maestro of mapping business requirements to the right AI/ML infrastructure and turning innovative ideas into deployable solutions. if AI and machine learning are your jam, and if you take pride in building top-tier AI platforms and services, we want to hear from you.

How Will You Shape the Future?

This role is all about hands-on technical prowess, and we expect you to bring your A-game. You'll be in the driver's seat, working with autonomy, accountability, and technical brilliance. Your mission includes:

Translating high-value AI/ML opportunities into the proper technical/platform investments. Serving as our AI/ML technology whisperer, guiding us towards the latest and greatest AI infrastructure and delivery trends. You'll be a guru behind our productization estimates and AI delivery platform. Crafting and integrating AI projects from the ground. Building proofs of concept and guiding our development team to the grand finale of implementation and deployment. You'll ensure scalability and top-tier performance. Locking arms with Data Engineers, Data Scientists, and Product/Program Managers. Together, you'll define, create, deploy, monitor, and document ML models and AI solutions that are both tailored and industry leading. Becoming our AI/ML technology evangelist. Get ready to shine on the conference stage, host webinars, and pen compelling white papers and blogs. Share your discoveries with clients and internal stakeholders, offering actionable insights that drive change.

Qualifications

To succeed in this role, you'll need:

A solid 7+ years of experience as Software Architect or ML Engineer, backed by a proven track record of successfully completed projects. Experience bringing incubated AI/ML solutions to production, including scoping, design, development, testing, deployment, and vigilant monitoring. Expertise in C#, Python, and the tools and libraries that make AI magic happen like Semantic Kernel, Langchain/LangGraph, Keras, TensorFlow, Pytorch, etc. Experience in creating and delivering multi-tenant cloud solutions at scale using Docker, Kubernetes and Cloud services. Experience with Azure stack will be an asset. Experience designing and implementing event-driven/microservices applications using Apache Kafka, Flink, etc. Solid understanding of agentic technologies and frameworks: AI agents, Assistants and RAG patterns, vector/hybrid search, etc. Exposure to model deployment and serving tools like Seldon Core, KServe, vLLM, etc. Experience with drift detection and adaptation techniques as well as evaluating metrics to drive optimizations using tools like Elastic stack, Prometheus, Alibi detect, etc. A solid background in DevOps and MLOps practices, and familiarity with tools to manage infrastructure as code, like Terraform and package managers like Helm Charts. Proficiency with pipeline orchestration tools, such as Airflow, Kubeflow, and Argo Workflows. Outstanding communication skills, ability to convey complex technical concepts to non-technical stakeholders and collaborate with cross-functional teams. A results-driven attitude, a passion for innovation, and a self-starting, proactive nature. You're organized, capable of juggling multiple tasks, and your creativity knows no bounds. You're a strategic thinker, always on the hunt for the next big thing.

Ready to make your mark? Join us on this exhilarating journey, where you'll be a vital part of our AI revolution. Let's transform the future together!

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.