AI Presales Architect

Role Summary The AI Presales Architect is responsible for driving AI, Data, Analytics, and Generative AI opportunities from opportunity qualification through solution architecture, proposal development, and successful transition to delivery teams. This role requires a strong blend of AI expertise, data engineering knowledge, cloud architecture experience, and customer-facing consulting skills to design scalable, business-driven solutions that deliver measurable outcomes. The ideal candidate will possess hands-on experience in Data Engineering, Analytics, Machine Learning, and Generative AI technologies, enabling them to architect end-to-end data-to-AI solutions across modern enterprise environments. AI Strategy & Solution Consulting • Engage with customers to understand business challenges, strategic objectives, and operational requirements. • Conduct discovery workshops, assessments, and AI ideation sessions to identify high-value AI and analytics opportunities. • Translate business requirements into scalable AI, Machine Learning, and Generative AI solution architectures. • Advise clients on AI adoption strategies, governance frameworks, operating models, and implementation roadmaps. • Develop business cases, ROI models, and transformation roadmaps to support AI investments. Generative AI & Agentic AI Solution Design • Design and architect enterprise-grade Generative AI solutions leveraging (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and prompt engineering techniques. • Develop and prototype AI agents and multi-agent systems using frameworks such as LangChain, LangGraph, CrewAI, or equivalent technologies. • Design autonomous workflows incorporating tool usage, memory management, planning, orchestration, and reasoning capabilities. • Evaluate emerging AI technologies, foundation models, and agentic AI frameworks to drive innovation and differentiation. • Define AI governance, security, responsible AI, and model lifecycle management practices. Data & Analytics Leadership • Assess client data landscapes and recommend appropriate data modernization strategies. • Collaborate with data engineering teams to define data architecture, ingestion, governance, and analytics frameworks. • Support AI/ML initiatives through data exploration, feature identification, model evaluation, and performance measurement. • Guide customers on data quality, data governance, and AI readiness assessments. • Drive integration of AI solutions with enterprise data platforms and business applications. Presales & Opportunity Management • Partner with Sales and Account teams to qualify opportunities and shape winning AI propositions. • Lead technical discussions, solution demonstrations, proof-of-concepts, and executive presentations. • Develop comprehensive proposals, Statements of Work (SOW), solution blueprints, effort estimations, and commercial responses. • Present solution recommendations to executive stakeholders and business decision-makers. • Support RFP, RFI, and tender responses related to AI, analytics, and digital transformation initiatives. • Contribute to pipeline growth and revenue generation through proactive customer engagement. Stakeholder & Practice Development • Act as a trusted advisor to customer stakeholders across business and technology functions. • Collaborate with delivery teams to ensure successful transition from presales to implementation. • Mentor consultants, architects, and AI engineers on emerging technologies and best practices. • Develop reusable solution accelerators, frameworks, and AI assets to improve delivery efficiency. • Represent the organization at industry events, conferences, workshops, and customer forums. • Stay current with advancements in AI, machine learning, cloud platforms, and industry trends. Required Skills & Experience • Hands-on experience with Python for AI, data analytics, and automation. • Experience developing solutions using Generative AI technologies, LLMs, and prompt engineering. • Knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, semantic search, and knowledge management architectures. • Experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalent. • Understanding of machine learning concepts, model evaluation, training pipelines, and AI operations. • Experience working with structured and unstructured data environments. • Consulting, business analysis, and solution architecture capabilities. • Ability to communicate complex AI concepts to both technical and non-technical audiences. • Experience conducting workshops, executive briefings, and stakeholder engagement sessions. • Proven ability to develop business cases and articulate measurable business outcomes. • Proposal writing, estimation, and commercial acumen • Experience in AI consulting, technology advisory, solution architecture, presales engineering, or digital transformation roles • Experience delivering enterprise AI, analytics, or machine learning initiatives. • Exposure to industries such as Financial Services, Government, Healthcare, Telecommunications, Manufacturing, Retail, or Transportation. • Experience leading Proof of Concepts (POCs), Minimum Viable Products (MVPs), and AI innovation programs. • Knowledge of Responsible AI, AI governance, security, compliance, and risk Management Frameworks