AI Technical Consultant, 60K

Tenth Revolution Group
1 year ago
Applications closed

Related Jobs

View all jobs

Hybrid Data Scientist Consultant — AI & Analytics

Freelance Spatial AI and Machine Learning Consultant

Consultant - Senior Consultant, Palantir Foundry Data Engineer, AI & Data, Defence & Security

Consultant - Senior Consultant, Palantir Foundry Data Engineer, AI & Data, Defence & Security

Generative AI & MLOps Architect

Consultant - Manager, Data Engineer, AI & Data, Defence & Security

AI Technical ConsultantLocation: Home-Based with Occasional TravelSalary: £55,000-£60,000 (+ 10% Bonus & Benefits)Who We're Looking ForMy client is seeking a skilled AI Technical Consultant to join their agile team. In this role, you'll provide expert guidance to clients, helping them implement and optimise AI technologies. Acting as a trusted advisor, you will collaborate with clients to understand their challenges, design tailored AI solutions, and oversee successful deployment and integration.This position supports flexible, remote working under my client's Winning from Anywhere® approach. While primarily home-based, you will occasionally travel to the Reading office for team tech days (every six weeks), quarterly conferences (Midlands), and occasional client events (mainly London).Key ResponsibilitiesWork closely with clients to identify challenges and opportunities that can be addressed with AI solutions.Design and develop AI models, algorithms, and frameworks tailored to client needs.Leverage expertise in machine learning, natural language processing, and computer vision to deliver impactful results.Analyse and preprocess data to prepare high-quality datasets for AI training.Deploy AI models into production environments, ensuring smooth integration and optimisation.Continuously monitor and refine AI model performance, implementing improvements as needed.Provide clients with training and best practice advice on AI tools and technologies.Stay current on advancements in AI, machine learning, and related fields to offer cutting-edge solutions.What You'll BringEssential Skills:Strong background in Microsoft data technologies with cloud platform experience (e.g., Azure, AWS, or Google Cloud).Advanced proficiency in Python.Demonstrated expertise in data science, machine learning, and AI technologies.Proven ability to work collaboratively and communicate technical concepts effectively in a consulting environment.Desirable Skills:Experience with Generative AI tools and frameworks, such as Co-pilot.Knowledge of optimisation techniques and design patterns for handling large-scale data solutions.What My Client Offers in ReturnMy client is committed to recognising and rewarding your contributions. Here's what's on offer:Competitive salary of £55,000-£60,000 with a 10% performance bonus.Flexibility through their Winning from Anywhere approach.25 days annual leave.Monthly home-working and setup allowances.Access to 24/7 GP services and an Employee Assistance Program.Private health insurance (after one year of service).Enhanced parental leave and pay.Participation in perks such as Cycle-scheme, Electric car schemes, and access to Perk-box for discounts and benefits.Interviews are currently underway, so if you are intrested or know someone who would be a great fit apply now or email me at: (url removed)

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.

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.

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.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.