TECHNICAL SALES ENGINEERS X 5 AI /ML

London
10 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist (Full Stack)

Data Scientist (Full Stack)

Senior Data Scientist (Applied AI)

Data Engineer

Data Engineer

Machine Learning Engineer (Mid-Senior, Remote)

Technical Sales Engineers / Solutions Engineers – AI & Machine Learning
Location: Remote UK with UK travel
Are you passionate about AI, machine learning, and driving real business impact? Do you thrive at the intersection of technology and sales? If so, this is your opportunity to join an industry-leading company shaping the future of AI-powered solutions.
Why Join Us?
ARCA is partnering with a cutting-edge AI company at the forefront of artificial intelligence and computer vision. This is more than just a sales role - it’s a chance to work with revolutionary AI technology, engage with leading enterprises, and drive digital transformation in an ever-evolving industry.
Your Role
As a Technical Sales Engineers / Solutions Engineers, you will be a key player in our growth, helping businesses understand and unlock the power of AI. Your responsibilities will include:
Driving Sales & Engagement – Connect with potential clients through email, calls, and social media, introducing our AI-driven solutions and assessing their needs.
Technical Consultation – Collaborate with customers to define project scopes, solve technical challenges, and maximize the value of AI implementations.
Customer Enablement – Deliver engaging webinars, presentations, and training sessions tailored to diverse audiences.
Sales Strategy & Business Development – Identify and develop new business opportunities, generating high-quality, sales-approved leads.
Demos & Presentations – Lead impactful technical demos that showcase the capabilities of AI and machine learning in solving real-world problems.
Cross-Functional Collaboration – Work closely with R&D, Sales, and Account Executives to align solutions with market needs and customer demands.
Market Insights – Report on customer challenges, competitive trends, and new opportunities to drive strategic growth.
What We’re Looking For
We want a high-energy, target-driven sales professional who can bridge the gap between complex AI technology and business value. The ideal candidate will have:
✔ 4+ years of experience in technical sales
✔ A strong track record in B2B sales, with a results-oriented mindset and a passion for closing deals.
✔ Excellent communication skills, capable of simplifying complex AI concepts for non-technical stakeholders.
✔ Nice to have: A Bachelor's degree in a technical field or equivalent hands-on experience.
✔ Nice to have: Familiarity with AI, computer vision, and machine learning markets.
What’s in It for You?
Work with groundbreaking AI solutions at the forefront of innovation.
Join a global team making an impact across industries.
Be part of an exciting, fast-growing company where your contributions matter.
Apply Now!
If this sounds like the opportunity you’ve been looking for, we’d love to hear from you! Submit your application today and take the next step in your AI sales career

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.