Architect, build, and launch robust enterprise-level artificial intelligence solutions leveraging the Microsoft Azure AI Foundry ecosystem, focusing heavily on Intelligent Document Processing (IDP).
Responsibilities
Architect and engineer enterprise AI and Generative AI applications utilizing Azure AI Foundry and Azure OpenAI.
Create Agentic AI systems, AI copilots, conversational chatbots, and RAG-driven solutions using frameworks such as LangChain, Semantic Kernel, and Copilot Studio.
Engineer advanced OCR and IDP workflows capable of managing structured, semi-structured, and unstructured data formats.
Execute document classification, entity recognition, key-value pair extraction, handwriting analysis, and the extraction of data from multi-page tables.
Leverage Azure AI Document Intelligence, Computer Vision, Form Recognizer, and proprietary OCR frameworks to drive solutions.
Requirements
Bachelor's degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or a closely related field
Master's degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or a closely related field
Build and operate reusable AIOps and LLMOps capabilities for enterprise AI solutions, automating model lifecycle, deployment, observability, and security on Microsoft Azure and Foundry.
Compensation
California - $96,000 - $156,730
Cleveland - $96,000 - $136,000
Colorado - $96,000 - $147,000
District of Columbia - $110,200 - $156,730
Illinois - $96,000 - $147,000
Maine - $96,000 - $136,000
Maryland - $96,000- $156,730
Massachusetts - $110,200 - $156,730
Minnesota - $96,000 - $136,000
New York / New Jersey - $118,000 - $167,633
Virginia - $96,000 - $136,000
Washington - $96,000 - $156,730
Responsibilities
Build and operate reusable AIOps and LLMOps capabilities
Automate lifecycle of models, prompts, agents, datasets, and deployment configurations
Create production automation using Python, PowerShell, Bash, C#, TypeScript, APIs, and infrastructure as code
Implement CI/CD, testing, environment promotion, approvals, rollback, versioning, and traceability through Azure DevOps and/or GitHub Actions
Establish observability for AI quality, reliability, performance, cost, safety, and business outcomes using Azure Monitor, Application Insights, OpenTelemetry, and MLflow
Requirements
Nice to have: Microsoft certification in Azure AI
Nice to have: Microsoft certification in Azure DevOps
Nice to have: Microsoft certification in Azure administration
Nice to have: Experience in management consulting
Nice to have: Experience with AI evaluation, tracing, telemetry, content safety
Define and lead the analytics strategy, executive reporting, governance, and AI innovation for a critical data center utilization and power reclamation function.
Compensation
Data Science M5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
Define the analytics vision, operating model, and multi-year roadmap for the critical data center utilization and power reclamation function
Own the authoritative measurement framework for power reclamation function, including metric definitions, targets, attribution, forecasts, and executive narratives
Lead development and governance heatmaps, metrics, trackers, alerting, etc.
Establish data contracts, lineage, quality controls, reconciliation standards, and decision rights across capacity, power, network, reservation, deployment, and financial data
Identify portfolio-level risks and opportunities, quantify business impact, and drive decisions with senior leadership and partner organizations
Requirements
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
AI only earns trust in government when it is accurate, explainable and secure. The Senior AI Engineer leads the design, build and deployment of production-grade AI and GenAI solutions on governed, high-quality data. They own the technical architecture and quality of AI solutions end to end, turning complex use cases into reliable applications that are measurable, auditable and compliant with UAE AI and data regulation.
Locations (3)
Abu Dhabi, Abu Dhabi, United Arab Emirates
Dubai, Dubai, United Arab Emirates
Sharjah, Sharjah, United Arab Emirates
Responsibilities
Lead the end-to-end design, development and deployment of AI/ML and GenAI applications, including RAG assistants, document intelligence, classification, prediction and Arabic/English NLP use cases, from use-case shaping through to production operations.
Architect enterprise-scale retrieval-augmented generation solutions using Azure OpenAI / Azure AI Foundry, Azure AI Search, vector indexing, hybrid retrieval and re-ranking, agentic and prompt orchestration, and grounding on curated Gold-layer data, optimising for accuracy, latency and cost.
AI only earns trust in government when it is accurate, explainable and secure. The Senior AI Engineer leads the design, build and deployment of production-grade AI and GenAI solutions on governed, high-quality data. They own the technical architecture and quality of AI solutions end to end, turning complex use cases into reliable applications that are measurable, auditable and compliant with UAE AI and data regulation.
Locations (2)
Abu Dhabi, Abu Dhabi, United Arab Emirates
Dubai, Dubai, United Arab Emirates
Responsibilities
Lead the end-to-end design, development and deployment of AI/ML and GenAI applications, including RAG assistants, document intelligence, classification, prediction and Arabic/English NLP use cases, from use-case shaping through to production operations.
Architect enterprise-scale retrieval-augmented generation solutions using Azure OpenAI / Azure AI Foundry, Azure AI Search, vector indexing, hybrid retrieval and re-ranking, agentic and prompt orchestration, and grounding on curated Gold-layer data, optimising for accuracy, latency and cost.
Define and operationalise responsible-AI controls covering explainability, bias testing, content safety, hallucination controls and human-in-the-loop review, aligned to the UAE Charter for the Development and Use of AI and Dubai's AI ethics principles, and present them to the Data Governance Council.
AI only earns trust in government when it is accurate, explainable and secure. The Senior AI Engineer leads the design, build and deployment of production‑grade AI and GenAI solutions on governed, high‑quality data.
Locations (2)
Abu Dhabi, Abu Dhabi, United Arab Emirates
Dubai, Dubai, United Arab Emirates
Responsibilities
Lead the end‑to‑end design, development and deployment of AI/ML and GenAI applications, including RAG assistants, document intelligence, classification, prediction and Arabic/English NLP use cases
Architect enterprise‑scale retrieval‑augmented generation solutions using Azure OpenAI / Azure AI Foundry, Azure AI Search, vector indexing, hybrid retrieval and re‑ranking, agentic and prompt orchestration, and grounding on curated Gold‑layer data
Define and operationalise responsible‑AI controls covering explainability, bias testing, content safety, hallucination controls and human‑in‑the‑loop review
Design secure, scalable APIs and integration patterns that connect AI capabilities with enterprise applications, portals and analytics
Define and operationalise responsible-AI controls covering explainability, bias testing, content safety, hallucination controls and human-in-the-loop review, aligned to the UAE Charter for the Development and Use of AI and Dubai's AI ethics principles, and present them to the Data Governance Council.
Design secure, scalable APIs and integration patterns that connect AI capabilities with enterprise applications, portals and analytics, with role-based access, data masking, private networking and identity controls enforced.
Partner with data architects and data engineers to define AI-ready data products, feature pipelines and data-quality gates, and ensure training and grounding data meets DQ and classification controls.
Design secure, scalable APIs and integration patterns that connect AI capabilities with enterprise applications, portals and analytics, with role-based access, data masking, private networking and identity controls enforced.
Partner with data architects and data engineers to define AI-ready data products, feature pipelines and data-quality gates, and ensure training and grounding data meets DQ and classification controls.
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