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- Tasks: Join a dynamic team to automate real workflows using AI and Python.
- Company: Exciting tech company focused on innovative AI solutions for high-stakes industries.
- Benefits: Collaborative office culture, flexible workdays, and hands-on experience with impactful projects.
- Other info: Work closely with sales and product teams in a vibrant London office.
- Why this job: Make a real difference by solving complex problems with cutting-edge technology.
- Qualifications: Strong Python skills and experience in building data/ML workflows.
The predicted salary is between 60000 - 80000 ÂŁ per year.
If you’re bored of “AI” that’s just a chatbox on top of GPT, this is for you. This could be your chance to work on a platform used by PE funds, insurers, and other high‑stakes teams to actually automate work on millions of documents – real workflows, not cool demos that die in slide decks.
We’re looking for an AI Solutions Engineer who loves three things:
- Writing Python
You’ll sit in the thick of it – pre‑sales, build, and post‑sales expansion – alongside sales, product and founders.
What you’ll actually do
- Jump into late‑stage deals and help figure out “what are we really solving here?”
- Design and build multi‑step, deterministic agents with humans in the loop where it matters
- Wire everything up: APIs, CRMs, spreadsheets, inboxes – whatever the customer actually uses
- Write Python to do the glue work: transforming schemas, running proprietary models, stitching weird edge cases together
- Keep 8–10 projects moving at once, making smart trade‑offs, not perfect architecture diagrams
Some flavour of projects
- A PE fund that gets ~300 pitch decks/reports a week – the team you would be joining helped them auto‑extract the numbers, run their LBO models in Python, and ship a partner‑ready deck every week
- Underwriting workflows that need full audit trails back to the original docs
- Real estate teams who want to ask a question about thousands of leases and get an answer – with citations – in seconds
You might be a fit if
- You write solid Python and actually enjoy debugging ugly real‑world data and integrations
- JSON, APIs and webhooks are just part of how you think about systems
- You’ve built data/ML/automation workflows before and can roughly sketch how they fit together
This is not for you if
- You want a pure research role or just to fine‑tune models all day
- You hate talking to customers, or need a fully scripted playbook
What you get
- A front‑row seat in one of the more interesting corners of applied AI
- A tight Solutions Engineering team in London that actually likes being in the office together
- A role where what you build is live, used, and directly tied to revenue and renewal
FAQ's
- Days in the office: 3 days a week (most of the team do 4 as they like working together)
If this sounds like you and you want to know more, send an email to oliver@seekr.inc with “Solutionize Is Not A Word” in the subject line. Include a CV if you have one ready, otherwise a LinkedIn profile and 3-4 lines in your own words as to why this role sounds interesting. All applications received as above will be reviewed by a human (me) and will get feedback. LinkedIn applications may not all be reviewed due to volume and lack of relevance.
AI Solutions Engineer - Live Automation for Real Deals employer: SEEKR
Contact Detail:
SEEKR Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Solutions Engineer - Live Automation for Real Deals
✨Tip Number 1
Get to know the company inside out! Research their products, values, and recent projects. This will help you tailor your conversations and show that you're genuinely interested in what they do.
✨Tip Number 2
Network like a pro! Reach out to current employees on LinkedIn or attend industry events. Building connections can give you insider info and might even lead to a referral.
✨Tip Number 3
Prepare for the technical side of things. Brush up on your Python skills and be ready to discuss real-world applications of your work. They want to see how you think on your feet!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining the team.
We think you need these skills to ace AI Solutions Engineer - Live Automation for Real Deals
Some tips for your application 🫡
Show Your Python Skills: Make sure to highlight your Python experience in your application. We want to see how you’ve used it in real-world scenarios, especially when it comes to debugging and working with messy data.
Be Clear About Your Experience: When you describe your past projects, focus on the specifics. Tell us about the workflows you've built and how they fit into larger systems. We love seeing how you think about integrations and automation!
Keep It Personal: In your email, don’t just send a generic CV. Share a few lines about why this role excites you. We’re looking for genuine interest, so let your personality shine through!
Apply Through Our Website: We encourage you to apply directly through our website. It helps us keep track of applications better and ensures you get the attention you deserve from our team!
How to prepare for a job interview at SEEKR
✨Know Your Python Inside Out
Make sure you brush up on your Python skills before the interview. Be ready to discuss your past projects and how you've used Python to solve real-world problems, especially in automation and data workflows.
✨Understand the Customer's Needs
Since this role involves a lot of customer interaction, think about how you can demonstrate your ability to understand and address customer pain points. Prepare examples of how you've successfully navigated customer requirements in previous roles.
✨Familiarise Yourself with APIs and Integrations
Get comfortable talking about APIs, JSON, and webhooks. You might be asked to explain how you've wired up different systems in the past, so have some concrete examples ready to showcase your experience.
✨Show Your Problem-Solving Skills
Be prepared to discuss how you approach complex problems, especially when it comes to debugging and integrating messy data. Think of specific instances where you've had to make trade-offs and how you prioritised tasks to keep multiple projects moving.