AI Solution Architect

Exciting Opportunity Alert! 🌟 HTC Global Services is hiring a AI Solution Architect for an 1-year extendable contract in Dubai, UAE (Onsite). HTC Global Services - a leading CMM level 5 global provider of innovative IT and Business Process Services and Solutions since 1990 with headquarters in Troy, Michigan, USA. Job Description Technical Skills Required • 10+ years of experience in technical roles such as Solution Architect, Technical Lead, or Senior Engineer, including recent hands-on experience designing and deploying LLM-based and agentic AI solutions into production environments. • Proven experience evaluating and introducing emerging technologies into enterprise environments, with sound judgment on build-versus-buy decisions, technology readiness, and identifying situations where AI may not be the appropriate solution. • Strong enterprise architecture and integration experience, including APIs, event-driven architectures, data pipelines, distributed systems, and cloud platforms. • Hands-on experience with Large Language Models (LLMs), agentic AI architectures, agent orchestration, Retrieval-Augmented Generation (RAG), vector databases, and Model Context Protocol (MCP) integrations. • Experience designing scalable, secure, and reliable AI solutions aligned with enterprise architecture, infrastructure, and data governance standards. • Experience delivering solutions in security-conscious or regulated environments, working closely with IT Security, Data Governance, Legal, and Enterprise Architecture teams. • Proficiency in Python, with experience in at least one additional programming language such as TypeScript, C#, or Java preferred. • Strong understanding of AI evaluation frameworks, prompt engineering, model performance assessment, observability, versioning, and production deployment practices. • Proven ability to assess AI models, platforms, and frameworks through structured experimentation and performance benchmarking against real business use cases. • Strong stakeholder management and communication skills, with the ability to translate business requirements into technical architectures and communicate solution value to executive leadership. • A background in machine learning or data science is an advantage but not mandatory. Job Description About the Role We are seeking an experienced AI Solution Architect to lead the design and implementation of enterprise-grade AI solutions powered by Large Language Models (LLMs) and agentic AI technologies. The role involves collaborating with business stakeholders to identify AI opportunities, translate business challenges into scalable technical architectures, and guide engineering teams in developing and deploying production-ready AI solutions. The architect will provide technical leadership across the AI solution lifecycle, from use case discovery and feasibility assessment to architecture design, implementation, deployment, and continuous improvement. This is a hands-on architecture role requiring active involvement in technical prototyping, solution validation, AI tooling, and technology evaluation. The successful candidate will maintain an evidence-based understanding of emerging AI technologies and recommend solutions that deliver measurable business value while meeting enterprise security, compliance, and scalability requirements. Key Responsibilities • Collaborate with business stakeholders to understand business challenges, identify AI opportunities, and translate requirements into effective technical solutions. • Lead AI use case discovery, feasibility assessments, solution qualification, technology evaluation, and implementation planning. • Own end-to-end solution architecture for LLM-based and agentic AI applications, including agent orchestration, RAG pipelines, MCP integrations, tool calling, and enterprise system integrations. • Design secure, scalable, reliable, and maintainable AI architectures aligned with enterprise data, infrastructure, security, and governance standards. • Develop hands-on prototypes to validate architectural decisions, evaluate technical feasibility, and mitigate implementation risks. • Evaluate emerging AI models, platforms, frameworks, and tools through structured experiments and benchmarks using real-world business scenarios. • Provide recommendations on technology adoption, build-versus-buy decisions, platform selection, and appropriate alternatives when AI is not the best solution. • Define architectural standards and reusable design patterns for AI applications to improve consistency, scalability, and delivery efficiency. • Establish best practices for prompt engineering, agent and prompt versioning, model evaluation, observability, release management, and production monitoring. • Define evaluation mechanisms to assess AI accuracy, reliability, safety, performance, cost, and business outcomes. • Lead and mentor AI engineers, review solution designs and code, resolve technical challenges, and ensure adherence to architectural standards. • Collaborate with IT Security, Data Governance, Legal, and Enterprise Architecture teams to address data privacy, security, compliance, auditability, and responsible AI requirements. • Maintain an overview of AI initiatives, prioritisation, implementation roadmaps, reusable components, and measurable business value. • Communicate technical architecture, design trade-offs, risks, and business benefits to engineering teams, senior stakeholders, and executive leadership. Expected Outcomes in the First Year • Establish a repeatable framework for moving AI use cases from discovery and assessment to secure production deployment. • Deliver multiple production-grade AI solutions with measurable business impact. • Establish consistent architectural standards, AI evaluation practices, and security controls. • Provide evidence-based recommendations for AI platforms, models, and technology investments. • Develop reusable AI components and architectural patterns to accelerate future implementations. • Enable engineering teams to deliver AI solutions with greater autonomy, reliability, and consistency. Interested candidates please do share your updated CV to [email protected] mentioning your Current CTC, expected CTC and notice period details. #HTCGlobalServices #ITJobs #hiring #lookingforjob #careers #jobs #immediatejoiner #recruitment #technology #jobseekers #interview #lookingforjobchange #newjob #uaejobs #contract #onsite #AIArchitect #AISolutionArchitect #RAG #LLM Show more

10+ years of experience in technical roles such as Solution Architect, Technical Lead, or Senior Engineer, including recent hands-on experience designing and deploying LLM-based and agentic AI solutions into production environments. Proven experience evaluating and introducing emerging technologies into enterprise environments, with sound judgment on build-versus-buy decisions, technology readiness, and identifying situations where AI may not be the appropriate solution. Strong enterprise architecture and integration experience, including APIs, event-driven architectures, data pipelines, distributed systems, and cloud platforms. Hands-on experience with Large Language Models (LLMs), agentic AI architectures, agent orchestration, Retrieval-Augmented Generation (RAG), vector databases, and Model Context Protocol (MCP) integrations. Experience designing scalable, secure, and reliable AI solutions aligned with enterprise architecture, infrastructure, and data governance standards. Experience delivering solutions in security-conscious or regulated environments, working closely with IT Security, Data Governance, Legal, and Enterprise Architecture teams. Proficiency in Python, with experience in at least one additional programming language such as TypeScript, C#, or Java preferred. Strong understanding of AI evaluation frameworks, prompt engineering, model performance assessment, observability, versioning, and production deployment practices. Proven ability to assess AI models, platforms, and frameworks through structured experimentation and performance benchmarking against real business use cases. Strong stakeholder management and communication skills, with the ability to translate business requirements into technical architectures and communicate solution value to executive leadership. A background in machine learning or data science is an advantage but not mandatory.

Collaborate with business stakeholders to understand business challenges, identify AI opportunities, and translate requirements into effective technical solutions. Lead AI use case discovery, feasibility assessments, solution qualification, technology evaluation, and implementation planning. Own end-to-end solution architecture for LLM-based and agentic AI applications, including agent orchestration, RAG pipelines, MCP integrations, tool calling, and enterprise system integrations. Design secure, scalable, reliable, and maintainable AI architectures aligned with enterprise data, infrastructure, security, and governance standards. Develop hands-on prototypes to validate architectural decisions, evaluate technical feasibility, and mitigate implementation risks. Evaluate emerging AI models, platforms, frameworks, and tools through structured experiments and benchmarks using real-world business scenarios. Provide recommendations on technology adoption, build-versus-buy decisions, platform selection, and appropriate alternatives when AI is not the best solution. Define architectural standards and reusable design patterns for AI applications to improve consistency, scalability, and delivery efficiency. Establish best practices for prompt engineering, agent and prompt versioning, model evaluation, observability, release management, and production monitoring. Define evaluation mechanisms to assess AI accuracy, reliability, safety, performance, cost, and business outcomes. Lead and mentor AI engineers, review solution designs and code, resolve technical challenges, and ensure adherence to architectural standards. Collaborate with IT Security, Data Governance, Legal, and Enterprise Architecture teams to address data privacy, security, compliance, auditability, and responsible AI requirements. Maintain an overview of AI initiatives, prioritisation, implementation roadmaps, reusable components, and measurable business value. Communicate technical architecture, design trade-offs, risks, and business benefits to engineering teams, senior stakeholders, and executive leadership.