Senior AI Engineer
Role Overview
Serve as the technical lead for a client engagement in Doha, owning platform design, integration, security, and delivery from discovery to production.
Work with engineering and solutions leadership to translate client needs into buildable designs on the company’s AI and decision-intelligence platform.
The role is hands-on and senior, involving prototyping, design and code review, and technical decision-making.
Key Skills for This Role
Full Job Posting
Role overview
Serve as the technical lead for a client engagement in Doha, owning platform design, integration, security, and delivery from discovery to production.
Work with engineering and solutions leadership to translate client needs into buildable designs on the company’s AI and decision-intelligence platform.
The role is hands-on and senior, involving prototyping, design and code review, and technical decision-making.
Architecture and delivery
- Lead client discovery, data and infrastructure audits, security and hosting assessments, feasibility reviews, proofs of concept on client data, and documentation of technical requirements.
- Own solution architecture, platform design, data flows, integrations, security, hosting, and use of platform APIs, agentic workflows, Azure, Databricks, and Elasticsearch.
- Design data ingestion, modelling, ontology and field-catalog integration, data reliability, partner gateway/API, and secure member-organisation data connections.
- Design hosting, access, data isolation, audit trails, and in-region hosting where data sovereignty requires it; own the security review.
- Lead Doha engineering delivery through go-live, including technical direction, sprint planning, design and code reviews, issue resolution, environment readiness, and handover.
- Estimate effort and capacity, expose scope and risk, flag technical risks, and assess changes before accepting scope changes.
- Act as the main technical counterpart for client IT, security, and data teams; explain trade-offs and challenge requests that endanger quality, security, or timelines.
- Create reusable reference architectures, integration patterns, security and hosting templates, and deployment runbooks.
Qualifications
- Master’s degree minimum in computer science, engineering, or a related field.
- 5+ years in software or data engineering, including 4+ years as a solutions or technical architect on client-facing data or AI platform delivery.
- Must have architected and taken at least one data or AI platform to production for an external client.
- Deep Azure architecture experience and hands-on experience with data platforms such as Databricks and Azure Data Factory and search such as Elasticsearch or equivalent.
- Understanding of production LLM and agentic systems, including RAG, tool use, evaluation, cost, and latency.
- Experience with knowledge graphs or ontologies, entity resolution, and integrating third-party and client data.
- Experience designing multi-tenancy, data isolation, access control, audit, and data residency in regulated or government environments.
- Strong API and integration design; ability to prototype in Python or TypeScript and review production code.
- Clear communication with engineers and senior non-technical stakeholders; able to lead without formal authority and structure ambiguity.
- Comfortable working on site as part of a small team. GCC, government or institutional, Arabic, energy, sustainability, financial-data, or embedded-delivery experience is valuable.
Technology stack
- Day-to-day technologies include Python, TypeScript, LangGraph, LangChain, LangSmith, Databricks, Azure Data Factory, Elasticsearch, and Azure Cloud.
- The wider platform includes Angular, NestJS, and Cypress.
Success milestones
Within 30 days, complete platform onboarding, lead the client data and infrastructure audit and hosting/data-sovereignty assessment, and map technical stakeholders and data owners.
Within 60 days, prove complex components on client data, agree technical requirements and scope, and begin design with open architecture questions resolved through technical spikes.
Within six months, sign off architecture, data, security, and integration design; deliver the scoped production platform for the client and first member organisations; and establish data isolation, audit trail, and support model.
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