At a Glance
- Tasks: Build AI agents that streamline revenue team tasks and tackle messy GTM data.
- Company: Join a pioneering tech company focused on innovative AI solutions.
- Benefits: Enjoy 25 days holiday, competitive salary, and remote work flexibility.
- Other info: Opportunity to lead a team and cultivate a culture of data excellence.
- Why this job: Be the first engineer shaping AI technology and making a real impact.
- Qualifications: 5+ years in backend development with a focus on data-heavy systems.
The predicted salary is between 60000 - 80000 £ per year.
We're building AI agents for GTM — agents that do the work of a revenue team end-to-end: researching accounts, drafting outreach, updating the CRM, triaging inbound, prepping calls, following up. GTM data is the messiest data in the enterprise. It lives across 10+ systems, it's stale, it's permissioned, and full of half-truths people typed into Salesforce on a Friday afternoon. Building agents on top of this without hallucinating and actually getting the job done at scale is the real engineering problem.
You'd be the first engineer. The hard part isn't the agent loop — it's the data and context layer underneath: pulling from Salesforce, HubSpot, Gong, Slack, Notion, Drive and a long tail of broken APIs, then turning that mess into the precise, fresh, permission-aware context an agent needs to take a real action. Most of your hardest engineering time will be on the data side: ingestion, modelling, freshness, retrieval quality, evals, latency, permissions.
Own the data and context pipeline end-to-end: connectors, ingestion, schema modelling, chunking, embedding, indexing, hybrid search, reranking, serving. Own the agent runtime on top: tool use, planning, multi-step execution, error recovery. Build the eval harness before shipping anything new. We don't ship on vibes. By month six you'll have hired the second engineer and shaped the team's culture around data quality, evals, latency, and product taste.
- 5+ years of production backend experience, with meaningful time on data-heavy systems — retrieval, search, recsys, streaming pipelines, or ML infra with real users and real SLAs.
- You think data first. Bad data in, bad agents out, and you've felt that pain.
- Shipped a non-trivial RAG, semantic search, or agent system end to end. Strong opinions on hybrid retrieval. You know when BM25 beats embeddings, when SQL beats both, and you've made those calls in production.
- (Nice to have) Python or Go fluency.
- Multi-tenant systems with row- or document-level access control. Messy GTM stack integrations — Salesforce, HubSpot, Gong, Outreach, Apollo — and the scars to show for it.
- Data governance or security overlap: lineage, audit trails, PII handling, prompt injection defence.
Founders call (60 min, remote). Walk us through a data-heavy system you've built.
25 days holiday + UK
Tech / AI Specialist in London employer: Stealth AI Company
As a Tech / AI Specialist at our innovative company, you'll be at the forefront of transforming messy GTM data into actionable insights, all while enjoying a collaborative and dynamic work culture that prioritises data quality and engineering excellence. With 25 days of holiday and opportunities for professional growth, including shaping your own team and influencing product direction, we offer a rewarding environment where your contributions truly matter. Join us in a location that fosters creativity and innovation, making it an exceptional place to advance your career in AI and data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Tech / AI Specialist in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to data-heavy systems. We want to see what you can do with messy data and how you’ve tackled real engineering challenges.
✨Tip Number 3
Prepare for the interview by diving deep into our tech stack. Familiarise yourself with Salesforce, HubSpot, and other tools we use. We love candidates who come in with knowledge about our challenges and how they can help solve them.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always on the lookout for passionate individuals who are ready to take on the challenge of building AI agents with us.
We think you need these skills to ace Tech / AI Specialist in London
Some tips for your application 🫡
Show Your Data-Driven Mindset:When you're writing your application, make sure to highlight your experience with data-heavy systems. We want to see how you've tackled messy data before and what strategies you've used to ensure quality. Remember, bad data in means bad agents out!
Be Specific About Your Experience:Don't just list your skills; give us the juicy details! Share specific projects where you've built or improved data pipelines, retrieval systems, or agent frameworks. We love hearing about the challenges you faced and how you overcame them.
Tailor Your Application:Make sure your application speaks our language! Use terms from the job description and relate your experience directly to the role of a Tech / AI Specialist. This shows us that you understand what we're looking for and how you fit into our vision.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss any important updates. Plus, it’s super easy to do!
How to prepare for a job interview at Stealth AI Company
✨Know Your Data Inside Out
Since the role revolves around messy GTM data, make sure you can discuss your experience with data-heavy systems in detail. Be ready to explain how you've handled data ingestion, modelling, and retrieval quality in past projects.
✨Showcase Your Engineering Mindset
Prepare to walk through a specific project where you built a non-trivial RAG or semantic search system. Highlight your decision-making process regarding hybrid retrieval methods and how you ensured data quality throughout.
✨Understand the Tech Stack
Familiarise yourself with the tools mentioned in the job description, like Salesforce, HubSpot, and Gong. If you've worked with these systems before, share your experiences and any challenges you faced integrating them.
✨Cultural Fit Matters
As you'll be shaping the team's culture, think about how you can contribute to a focus on data quality and product taste. Prepare examples of how you've fostered a positive engineering culture in previous roles.
