Solution Architect - Presales, Software Delivery & AI

Mercurial Minds is looking for a seasoned Solution Architect with strong client-facing experience to bridge business needs, technology strategy, presales, and software delivery. The ideal candidate will have a solid background in software engineering and architecture, combined with hands-on expertise in generative AI, large language models, AI agents, machine learning, intelligent automation, and cloud AI platforms. This role involves active participation across executive and customer discussions, discovery workshops, proposal development, architecture design, estimation, technical decision-making, and delivery governance. The position is based onsite in Islamabad and is a full-time opportunity.

Key Responsibilities

Lead customer discovery sessions, technical workshops, presentations, demonstrations, and solution design meetings. Translate business goals, processes, constraints, and requirements into practical, feasible solutions. Develop comprehensive solution architectures, roadmaps, technical proposals, RFP/RFI responses, statements of work, proofs of concept, estimates, and delivery plans. Evaluate opportunities for feasibility, cost, security, scalability, maintainability, delivery risk, and strategic alignment. Clearly communicate technical decisions and trade-offs to business, technical, and commercial stakeholders. Support resource planning, pricing, and commercial reviews, ensuring smooth transition of presales commitments into delivery.

Design secure, scalable, resilient, maintainable, and cost-effective solutions across web, mobile, cloud, data, integration, automation, and AI domains. Define system components, service boundaries, APIs, integrations, data flows, deployment models, security controls, and operational requirements. Select appropriate technologies and architectural patterns based on customer needs rather than market trends. Produce architecture diagrams, specifications, interface definitions, decision records, and implementation guidelines. Collaborate closely with project managers, business analysts, engineering, QA, DevOps, security, data teams, and customer stakeholders. Provide architectural leadership throughout implementation, testing, deployment, transition, stabilization, and complex issue resolution. Review critical deliverables, identify risks and dependencies early, and convert delivery knowledge into reusable reference architectures, templates, accelerators, and service offerings.

Identify where AI technologies can deliver measurable value versus conventional software or automation. Design AI solutions leveraging generative AI, large language models, retrieval-augmented generation (RAG), AI agents, machine learning, document intelligence, conversational interfaces, and intelligent automation. Evaluate commercial and open-source AI models, cloud AI services, APIs, orchestration frameworks, vector databases, and infrastructure. Define model selection, prompting, grounding, evaluation, guardrails, observability, privacy, security, and human oversight approaches. Assess accuracy, latency, scalability, compute and token consumption, cost, regulatory exposure, and operational supportability. Integrate AI securely with enterprise applications, data platforms, APIs, and workflows, guiding solutions from proof of concept to reliable production deployment. Stay current with AI ecosystem developments and translate insights into practical recommendations for customers and internal teams.

Required Qualifications

A minimum of 8 years’ experience in software engineering, architecture, technical consulting, or enterprise delivery, including at least 3 years in a solution architect, technical lead, or similar customer-facing role. Proven track record in technical presales and delivering medium-to-large software projects. Strong expertise in distributed systems, APIs, integrations, databases, security, cloud-native architecture, scalability, reliability, DevOps, CI/CD, containers, infrastructure, observability, and production operations. Hands-on experience with Azure, AWS, or Google Cloud platforms and a practical understanding of modern AI architectures and the generative AI ecosystem. Proficiency in creating architecture diagrams, proposals, estimates, implementation plans, and executive presentations. Excellent analytical, problem-solving, communication, stakeholder management, and influencing skills. Bachelor’s degree in a relevant discipline or equivalent practical experience.

Preferred Qualifications and Experience

Experience delivering generative AI, machine learning, data, or intelligent automation solutions. Familiarity with Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI APIs, open-source models, vector databases, or agentic frameworks. Background in microservices, event-driven systems, enterprise integration, data engineering, analytics, robotic process automation (RPA), or workflow automation. Experience in consulting, professional services, systems integration, or multi-customer delivery environments. Proven ability to convert proofs of concept into production-ready solutions and mentor engineering teams. Relevant certifications in architecture, cloud, security, data, or AI are a plus.

Core Competencies

Customer orientation, commercial awareness, architectural depth, delivery pragmatism, structured problem-solving, persuasive communication, ownership, sound judgment, collaborative leadership, continuous learning, and responsible AI use.

Measures of Success

Successful discovery and conversion of qualified opportunities into deliverable projects. Accurate estimates and strong alignment between presales commitments and delivery outcomes. Delivery of secure, scalable, and cost-effective solutions that meet business and operational needs. Early identification and mitigation of risks and dependencies. Advancement of AI concepts and proofs of concept into production environments. Increased reuse of architectural assets and enhanced confidence among customers, commercial teams, and delivery teams.