Lead AI Engineer
Role Overview
Own AI projects from customer discovery to reliable production delivery, staying hands-on, making architecture decisions, and helping engineers deliver quality work.
Based in Abu Dhabi with hybrid arrangements agreed for the relevant opportunity.
Permanent and contract opportunities are available; scope depends on experience, interests, and team needs.
Key Skills for This Role
Full Job Posting
The role
Own AI projects from customer discovery to reliable production delivery, staying hands-on, making architecture decisions, and helping engineers deliver quality work.
Based in Abu Dhabi with hybrid arrangements agreed for the relevant opportunity.
Permanent and contract opportunities are available; scope depends on experience, interests, and team needs.
About Scale Force Cloud
Builds production AI systems, data platforms, and cloud solutions for financial services and other industries.
What you will do
- Translate customer needs into scoped solutions, plans, and technical decisions; demonstrate progress and flag risks early.
- Build LLM applications, RAG pipelines, and agent workflows with tool use, APIs, and business-system integrations.
- Design SQL, PostgreSQL, and vector-search retrieval and data flows; improve relevance, data quality, and access controls.
- Deploy and operate cloud applications with testing, CI/CD, observability, and incident debugging.
- Build evaluation datasets and regression checks to improve answer quality, reliability, latency, and cost.
- Protect sensitive data through permissions, secure integrations, auditability, and appropriate human approval.
- Review code and architecture, mentor engineers, and provide clear documentation and customer handovers.
- May contribute to technical hiring, proposals, reusable demos, and cloud partnerships.
What you bring
- Strong Python and software engineering foundations, including maintainable code, APIs, testing, Git, and debugging.
- Typically 4+ years of relevant engineering experience; ownership evidence is more important than tenure alone.
- Production LLM application delivery with clear examples of personal contribution, trade-offs, and lessons learned.
- Practical retrieval, tool-calling, or agent-orchestration experience and ability to evaluate AI behavior beyond API success.
- Production deployment and support experience on AWS, Azure, or GCP.
- Ability to lead a technical workstream, review work, and explain complex decisions to customers.
- Effective use of AI coding assistants with careful review, testing, and validation of generated code.
- Professional English; French is relevant for French-speaking customer engagements.
Useful additional experience
- AWS Bedrock or Azure AI, LangGraph or similar frameworks, Model Context Protocol, Docker, or Kubernetes.
- Financial services or other regulated environments; technical interviewing, mentoring, or pre-sales.
Compensation
- Compensation is discussed according to experience and scope.
What we offer
- Ownership of production AI projects and influence over technical direction, with collaboration across EMEA.
- Permanent team members may receive professional development and certification support, coding-assistant tooling, and dedicated hardware according to the agreed package.
Application
- Applicants are asked to share a CV, availability, location, and a short example of an AI system they delivered, describing their personal contribution.
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