AI Solutions Architect Pre Sales (UAE)

Role purpose The AI Solution Engineer is a senior, hands-on member of the pre-sales team responsible for shaping credible enterprise AI solutions and proving that they can work. The role combines technical discovery, architecture, prototyping, estimation and client presentation. The successful candidate must be able to move comfortably from an executive use-case discussion to building and demonstrating a working solution on the Microsoft AI stack. Please note, this role is open to candidates based in both the UAE and India locations. Key responsibilities • Lead technical discovery with client business, data, security and technology stakeholders; convert business priorities into qualified AI use cases, solution requirements and a practical delivery approach. • Design secure, scalable enterprise architectures for generative AI, agentic AI, conversational AI, intelligent search, document intelligence and AI-enabled applications using the Microsoft cloud stack. • Build working demonstrations, accelerators and proof-of-concepts using production-relevant patterns not only mock-ups to validate feasibility, user experience, integration and expected value. • Own the technical workstream of RFPs, RFIs and proposals, including architecture, solution narrative, scope, assumptions, dependencies, non-functional requirements, effort estimates, bill of materials and delivery risks. • Define RAG, grounding, tool-use, memory, orchestration, evaluation, observability and human-in-the-loop patterns appropriate to each use case. • Assess model and platform choices across quality, latency, throughput, security, data residency, responsible AI and cost; explain the trade-offs clearly to both technical and executive audiences. • Present solutions in workshops, solution reviews, demonstrations and bid presentations; respond credibly to detailed questions from enterprise architecture, cybersecurity and procurement teams. • Work with account, industry and delivery teams to shape opportunities, identify reusable assets and position differentiators without overcommitting scope or capability. • Support technical due diligence, implementation planning and handover after award, ensuring that proposal commitments are understood, feasible and traceable into delivery. • Maintain current knowledge of Microsoft AI services, reference architectures, licensing and emerging agentic patterns, and turn that knowledge into reusable pre-sales assets. Required experience and capabilities • 10+ years of professional experience across software engineering, cloud architecture, AI/ML engineering or enterprise solution consulting. • At least 5 years of hands-on experience specifically designing and building generative AI solutions using large language models, including enterprise RAG and/or AI agents. • Demonstrable experience delivering AI or advanced analytics solutions beyond proof-of-concept stage, with a strong understanding of production engineering, security, monitoring and operational support. • Strong hands-on development capability in Python and APIs/SDKs; experience with C#/.NET is advantageous. • Deep understanding of LLM application patterns: prompt and context engineering, embeddings, vector and hybrid retrieval, tool/function calling, structured outputs, agent orchestration, evaluation and guardrails. • Experience integrating AI solutions with enterprise applications, APIs, databases, document repositories and identity platforms. • Strong knowledge of cloud security, Entra ID, managed identities, private networking, secrets management, role-based access control, data privacy and responsible AI controls. • Proven client-facing experience in discovery, solution presentations, technical writing, estimation and proposal development for complex enterprise opportunities. • Ability to produce clear architecture diagrams, sequence flows, assumptions, sizing models and implementation plans under bid deadlines. • Excellent written and verbal English; able to challenge requirements constructively and communicate complex concepts without unnecessary jargon. Preferred qualifications • Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103), Azure Solutions Architect Expert (AZ-305), or equivalent current Microsoft credentials. • Experience in regulated or government environments, including security review, data residency and private-cloud/hybrid integration requirements. • Experience estimating Azure AI consumption, model throughput/capacity, search capacity, infrastructure and licensing for commercial proposals. • Experience creating reusable accelerators, reference architectures or demo environments for an AI consulting or technology pre-sales organisation. Show more Show less

Required experience and capabilities: 10+ years of professional experience across software engineering, cloud architecture, AI/ML engineering or enterprise solution consulting. At least 5 years of hands-on experience specifically designing and building generative AI solutions using large language models, including enterprise RAG and/or AI agents. Demonstrable experience delivering AI or advanced analytics solutions beyond proof-of-concept stage, with a strong understanding of production engineering, security, monitoring and operational support. Strong hands-on development capability in Python and APIs/SDKs; experience with C#/.NET is advantageous. Deep understanding of LLM application patterns: prompt and context engineering, embeddings, vector and hybrid retrieval, tool/function calling, structured outputs, agent orchestration, evaluation and guardrails. Experience integrating AI solutions with enterprise applications, APIs, databases, document repositories and identity platforms. Strong knowledge of cloud security, Entra ID, managed identities, private networking, secrets management, role-based access control, data privacy and responsible AI controls. Proven client-facing experience in discovery, solution presentations, technical writing, estimation and proposal development for complex enterprise opportunities. Ability to produce clear architecture diagrams, sequence flows, assumptions, sizing models and implementation plans under bid deadlines. Excellent written and verbal English; able to challenge requirements constructively and communicate complex concepts without unnecessary jargon.

Lead technical discovery with client business, data, security and technology stakeholders; convert business priorities into qualified AI use cases, solution requirements and a practical delivery approach. Design secure, scalable enterprise architectures for generative AI, agentic AI, conversational AI, intelligent search, document intelligence and AI-enabled applications using the Microsoft cloud stack. Build working demonstrations, accelerators and proof-of-concepts using production-relevant patterns not only mock-ups to validate feasibility, user experience, integration and expected value. Own the technical workstream of RFPs, RFIs and proposals, including architecture, solution narrative, scope, assumptions, dependencies, non-functional requirements, effort estimates, bill of materials and delivery risks. Define RAG, grounding, tool-use, memory, orchestration, evaluation, observability and human-in-the-loop patterns appropriate to each use case. Assess model and platform choices across quality, latency, throughput, security, data residency, responsible AI and cost; explain the trade-offs clearly to both technical and executive audiences. Present solutions in workshops, solution reviews, demonstrations and bid presentations; respond credibly to detailed questions from enterprise architecture, cybersecurity and procurement teams. Work with account, industry and delivery teams to shape opportunities, identify reusable assets and position differentiators without overcommitting scope or capability. Support technical due diligence, implementation planning and handover after award, ensuring that proposal commitments are understood, feasible and traceable into delivery. Maintain current knowledge of Microsoft AI services, reference architectures, licensing and emerging agentic patterns, and turn that knowledge into reusable pre-sales assets.