Job description
Lead AI Engineer — Abu Dhabi (Hybrid)
About us
Scale Force Cloud builds production AI systems, data platforms and cloud solutions for clients in financial services and other industries.
The role
Own AI projects from customer discovery to reliable production delivery. You will stay hands-on, make architecture decisions, communicate directly with customers and help other engineers deliver high-quality work. Permanent and contract opportunities are available. The scope will reflect your experience, interests and the needs of the team. Based in Abu Dhabi, with hybrid working arrangements agreed for the relevant opportunity.
What you will do
- Translate customer needs into scoped solutions, delivery plans and clear technical decisions; demonstrate progress and flag risks early.
- Build LLM applications, RAG pipelines and agent workflows with reliable tool use, APIs and business-system integrations. Choose simpler approaches when they meet the need.
- Design retrieval and data flows using SQL, PostgreSQL and vector search; improve relevance, data quality and access controls.
- Deploy and operate cloud applications with testing, CI/CD, observability and effective incident debugging.
- Build evaluation datasets and regression checks; improve answer quality, reliability, latency and cost using measured results.
- Protect sensitive data through application-level permissions, secure integrations, auditability and appropriate human approval.
- Review code and architecture, mentor engineers and maintain clear documentation and customer handovers.
- Depending on your strengths, contribute to technical hiring, proposals, reusable demos and cloud partnerships.
What you bring
- Strong Python and software engineering foundations: maintainable code, APIs, testing, Git and debugging. Typically 4+ years of relevant engineering experience, with evidence of ownership more important than tenure alone.
- Hands-on delivery of an LLM application used in production, with clear examples of your contribution, trade-offs and lessons learned.
- Practical experience with retrieval, tool calling or agent orchestration, and evaluating AI behaviour beyond successful API responses.
- Experience deploying and supporting production workloads on at least one major cloud: AWS, Azure or GCP.
- Ability to lead a technical workstream, review others’ work and explain complex decisions clearly to customers.
- Effective use of AI coding assistants, with careful review, testing and validation of generated code.
- Professional English. French is relevant to French-speaking customer engagements.
Useful additional experience
AWS Bedrock or Azure AI; LangGraph or similar orchestration frameworks; Model Context Protocol (MCP); Docker and Kubernetes; financial services or other regulated environments; technical interviewing, mentoring or pre-sales.
What we offer
- Ownership of production AI projects and influence over technical direction.
- Collaboration with engineers across EMEA.
- For permanent team members: professional development and certification support, coding-assistant tooling and dedicated hardware, according to the agreed package.
- Compensation discussed according to experience and scope.
To apply
Share your CV, availability, location and a short example of an AI system you have delivered. Tell us what you personally owned and whether you also enjoy mentoring, hiring or developing new solutions. Please omit confidential customer information.
Requirements added by the job poster
- 2+ years of work experience with Cloud Computing
About the company
Scale Force Cloud was founded by an ex-AWS Engineer to help teams build, ship, and scale AI on AWS. With experience across the US, Europe, the Middle East, and Africa—and more than a dozen client engagements—we design secure, scalable cloud foundations and production-grade AI systems.
Services
- AWS cloud architecture and engineering
- MLOps & LLMOps (CI/CD, evaluation, monitoring, governance)
- Agentic AI (RAG, tool use, orchestration)
- LLM cost optimization (model selection, routing, caching, infra tuning)
- Machine learning (non‑generative)
