Summary
Echelon is hiring a Head of Applied AI to lead its applied AI engineering team, technical direction and delivery quality. The role covers architecture and model choices, team building, production deployments, technical risk, enterprise pre-sales, security standards and customer handovers. Candidates should have at least 6 years of engineering experience, a record of leading teams that deliver production systems, and hands-on depth with LLMs and large-scale systems. The position is based in London with regular time in Riyadh, or in Riyadh; compensation includes base salary, performance bonus, medical cover and equity, with terms shared at offer stage.
The posting
As published on echelon.inc. Pulled from the employer's site and shown as they wrote it.
The Role: Leading Applied AI
Reporting to the Managing Director, you own Echelon’s applied AI engineering: the team, the technology and the technical quality of everything we deploy. You own the customer and build what is deployed against their most critical workflows, running every engagement as one team with the Head of AI Transformation.
Technical direction: Define the technical direction of every engagement: architecture, model and partner choices, and the build, partner or buy decisions behind them.
Build the organisation: Hire and lead forward deployed engineers and their leads; design pods sized to signed demand, the hiring bar, the career paths and an engineering culture people want to join, with a deliberate pipeline of engineering talent in the region.
Answer for delivery: Own the technical quality of every deployment from prototype to production, and sign off scope, architecture and technical risk before Echelon commits to a customer. Stay close enough to live systems to review designs, unblock critical issues and lead incident reviews.
Support the sale: Be the senior technical counterpart in strategic pursuits: architecture, security and data-residency reviews, demos and solution design with customer CIOs and CISOs. Shape how engagements are scoped and priced.
Set the standards: Set how Echelon builds, secures and operates AI systems, with the security and data baseline every deployment meets, aligned with data-protection and security regulations, and hold delivery partners to it.
Transfer capability: Make sure every deployment leaves the customer able to run it: documentation, evaluations, monitoring, training and a clean handover.
Shape the platform: Work with the platform lead to shape Echelon’s platform roadmap and its evolution over time, bringing repeated delivery patterns and customer needs back from the field.
Shape the ecosystem: Lead the technical relationship with technology partners and model providers across Echelon’s engagements.
AI and Technology First
Leadership record: You have 6+ years in engineering, including building and leading teams that ship production systems, as a senior leader in an AI company, a forward deployed business, or as a technical founder.
Builder’s record: You have taken a product or platform from zero to real users at scale, and know the difference between a demo and a system an enterprise depends on.
AI-native depth: Hands-on with LLMs, agent frameworks, MCP, retrieval and evaluation, with a clear view of how to make AI systems observable, safe and reliable at scale. You still go hands-on when it matters.
Systems at scale: Command of distributed systems, data engineering, and cloud and on-premise infrastructure under strict data residency and security constraints.
Enterprise delivery: You have led complex deployments for large enterprises or government, including technical pre-sales, scoping and the commercial trade-offs behind them.
Executive credibility: Customer technology leaders, partners and investors trust your judgement, and you can explain a trade-off to an engineer and to a board on the same day.
Strong Advantage
Academic depth: A PhD in AI, Machine Learning or Computer Science.
Regional experience: Delivery in Saudi Arabia or the GCC, ideally in the public sector or regulated industries, with working knowledge of PDPL, NCA and SDAIA frameworks.
Arabic & multimodal: Experience building Arabic NLP, speech or multimodal systems. Arabic language is a plus.
Open-weight models: Fine-tuning, post-training or serving open-weight models on private or on-premise infrastructure.
Partnerships: Experience leading technical relationships with model providers, cloud providers or technology partners.
Our Values
Customer first: You measure your work by customer outcomes, and prioritise accordingly.
Ownership mentality: You own outcomes end to end. When something breaks or stalls, you treat it as yours to fix.
Clear communicator: You explain complex systems simply, to operators and executives alike, and make the value of your work visible.
Comfortable with ambiguity: You make progress with incomplete information, partial data and unfamiliar environments, and you enjoy solving problems where there is no structure yet.
Long-term builder: You invest in Echelon’s long-term success, bringing field insight back into our strategy and operations.
Builds others: You measure yourself by the engineers you develop and the capability you leave behind with customers.
The Seat
Location: London, with regular time in Riyadh, or Riyadh
Reports to: The Managing Director
Travel: Regular time on customer sites across the region and with partners abroad.
Package: A base salary, performance bonus, medical cover, and equity through the employee equity programme. Terms are shared at offer stage.
How We Work
You start from the problem, not the tool. You meet an unclear brief or a broken environment with energy and a plan.
Curiosity: You learn how the work really happens on site and keep learning as the stack moves.
Speed: You move from brief to a working system quickly and do not wait for perfect conditions.
Rigour: You reproduce before you fix, test what you ship and can explain every choice.
Confidence & humility: You challenge and accept challenge, and change your view on evidence, without ego.
Autonomy: You decide with incomplete information and own it; from an outcome, you organise the work yourself.
Resourcefulness: Missing access, partial data, an unfamiliar codebase: you find a way with what is available.
Situational leadership: You lead when the moment calls for it, and follow when someone else has the better call.
A force multiplier: Your reviews, patterns and documentation make the whole bench faster.
AI-native depth: AI tools are part of how you write, review and debug code and how you run the organisation, and you have built something real with an LLM.
Where you are: A senior engineering leader who stays close to the code: fluent in Python, TypeScript or similar, and able to review what ships