Statistical AI Solution architect

AI Solution Architect Statistical AI and Generative AI The core focus should be business-to-technical mapping, RFP response, solution sizing, pricing, playbooks, and repeatable solution offerings for Statistical AI, Generative AI, and Agentic AI areas. The role should translate business problems into scalable AI solution constructs, covering use-case discovery, data and model architecture, deployment patterns, governance, cost estimation, and value realization across enterprise environments.

Key Responsibilities:

• Define and package repeatable Statistical AI and Generative AI solution offerings for enterprise use cases such as forecasting, anomaly detection, classification, optimization, document intelligence, conversational AI, copilots, RAG-based knowledge assistants, and agentic workflows.
• Translate business requirements into technical solution blueprints covering data sources, feature engineering, model selection, GenAI orchestration, integration patterns, security, governance, deployment architecture, and operating model.
• Develop solution playbooks, discovery questionnaires, maturity assessment frameworks, reference architectures, and value-realization templates to guide client engagements from advisory and assessment through pilot, production deployment, and scale-up.
• Lead RFP and proposal responses by shaping the functional, technical, delivery, security, compliance, and commercial aspects of Statistical AI, GenAI, and Agentic AI solutions.
• Design architecture narratives and solution constructs for AI/ML pipelines, MLOps, LLMOps, model monitoring, evaluation, prompt management, vector databases, API integration, and enterprise application embedding.
• Create estimation models for solution sizing, including data preparation effort, model development, accelerator reuse, infrastructure needs, licensing, cloud or on-premise deployment costs, implementation timelines, and Total Cost of Ownership.
• Collaborate with cloud providers, data platform teams, AI platform vendors, open-source ecosystem partners, hardware OEMs, and system integrators to assemble scalable, secure, and cost-effective AI solutions.
• Estimate delivery team structures, skill requirements, timelines, and governance mechanisms for implementing Statistical AI, GenAI, multimodal AI, and agentic AI solutions across cloud, hybrid, on-premise, and controlled enterprise environments.

Educational Qualifications:

• Masters degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• Advanced degree, executive certification, or specialization in AI/ML, Generative AI, Cloud Architecture, Data Engineering, or Enterprise Architecture is preferred.
• Relevant certifications in cloud platforms, AI/ML services, data platforms, MLOps, LLMOps, or solution architecture will be an added advantage.
• Strong foundation in statistical modeling, machine learning concepts, GenAI solution patterns, enterprise data architecture, and responsible AI practices is expected.