Data & AI Solution Architect (Hiring Immediately)

OBSS
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist-Senior Manager

Data Scientist-Manager

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Head of DevOps and DataOps

Data Scientist: Graph Database & Ontology Specialist

Data Scientist – Fraud Strategic Analytics Associate

About Us

At OBSS Technology, we empower organizations to achieve their goals through innovative, data-driven, and AI-focused solutions. With expertise in large-scale enterprise projects, we leverage cutting-edge technologies to transform business processes and deliver exceptional value. We are seeking an experienced Data & AI Solution Architect to join our team and spearhead transformative retail and e-commerce projects.


Role Overview

As a Data & AI Solution Architect, you will drive the design and delivery of cutting-edge solutions that align with our clients' business strategies. You will oversee the end-to-end technical roadmap, lead architectural decisions, and ensure successful project execution. This role will focus on delivering impactful data and AI solutions, integrating modern machine learning techniques, and driving innovation within the retail and e-commerce domain.


Responsibilities

• Drive the design and implementation of large-scale data architectures and analytics projects for a retail/e-commerce client.

• Incorporate advanced Data Science and AI capabilities such as Natural Language Processing (NLP), Computer Vision, Generative AI (GenAI) and LLM into client projects.

• Translate complex business requirements into technical solutions, ensuring seamless integration into existing systems.

Qualifications

• Bachelor’s or higher degree in a relevant field such as Computer Science, Software Engineering, Data Science, Mathematics, Statistics, or related disciplines.

• Minimum 8 years of experience in Data Science, Machine Learning, Deep Learning and Advanced Data Analytics projects.

• Hands-on experience with modern data and ML platforms, cloud AI/Data services, major open source tools and libraries.

• Experience with MLOps practices (e.g., MLflow, Kubeflow) is a plus.

• Strong communication skills for translating technical concepts to non-technical stakeholders.

• Experience in a number of Retail, CRM, Marketing use cases (e.g., advanced personalization, recommendation systems, segmentation, campaign targeting, CLV, churn analysis) is required.

• Having industry certifications in AI, data analytics, or data science is a plus.

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

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

Industry Insights

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

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.