AI Pre-Sales Consultant & Solution Architect

AI Pre-Sales Consultant & Solution Architect

Experience: 9 yrs - 15+ yrs

Location

Work Mode

Role Overview

The AI PreSales Consultant & Solution Architect is responsible for shaping and positioning AI solutions with customers, translating business problems into robust AI architectures, and supporting the AI CoE in defining and executing the AI strategy. The role combines customerfacing presales, solution architecture, and CoE enablement.

Key responsibilities

Strategy and AI CoE support

• Partner with the Head of AI CoE to refine and execute the AI strategy, including target industries, key use cases, and gotomarket offerings.
• Contribute to defining AI solution blueprints, reference architectures, accelerators, and reusable components that can be industrialized across customers.
• Track market trends, competitive offerings, and emerging AI/GenAI technologies to provide strategic inputs into the CoE roadmap.
• Help define standards and guardrails for responsible AI, security, compliance, and governance in all proposed solutions.

Customer discovery

• Engage with customer stakeholders (business, IT, data, security) to understand business objectives, pain points, and current landscape.
• Lead and facilitate discovery workshops to elicit detailed functional and nonfunctional requirements, success criteria, and constraints.
• Translate business problems into welldefined AI use cases, solution options, and phased roadmaps.
• Document current state, target state, and gaps in a form consumable by both business and technical teams.

AI solution architecture

• Design endtoend AI/ML and Generative AI architectures covering data ingestion, feature engineering, model development, orchestration, integration, and monitoring.
• Select appropriate AI techniques (ML, GenAI/LLM, NLP, CV, RAG, optimization, etc.), platforms (cloud services, MLOps/LLMOps), and integration patterns based on customer context.
• Define nonfunctional aspects such as scalability, performance, resilience, observability, security, privacy, and compliance.
• Create architecture artifacts: solution architecture diagrams, data flow diagrams, deployment topologies, integration specifications, and highlevel design documents.
• Collaborate with delivery, data engineering, and platform teams to validate feasibility, effort estimates, and implementation risks.

Presales, proposals, and deal support

• Act as the technical lead in presales pursuits, working closely with Sales, Bid Management, and the Head of AI CoE.
• Own the technical response for RFPs/RFIs: solution approach, architecture, scope, assumptions, risks, and dependencies.
• Develop highquality proposals, SoWs, and effort/pricing models in partnership with finance and delivery leaders.
• Design and lead PoCs/PoVs or pilots to derisk critical solution components and demonstrate value.
• Ensure a clean handover from presales to delivery with clear scope, architecture, and success metrics.

Customer presentations and storytelling

• Create compelling customerfacing presentations that articulate business value, solution design, implementation roadmap, and differentiated CoE capabilities.
• Tell a strong problem solution value roadmap story tailored to executive, business, and technical audiences.
• Deliver demos of AI capabilities (including GenAI/LLM use cases) and walkthroughs of architectures in workshops and steering forums.
• Support thought leadership by contributing to case studies, whitepapers, webinars, and conference presentations representing the AI CoE.
• Maintain reusable proposal templates, architecture patterns, and estimation models to improve quality and speed of future pursuits.

Internal enablement and collaboration

• Coach sales and account teams on positioning AI/GenAI offerings and articulating value to customers.
• Work with delivery teams to capture learnings from projects and feed them back into the AI CoE playbooks and accelerators.
• Mentor junior architects, data scientists, and consultants on solutioning and customerfacing skill

Required experience

• Total experience: typically 9 15+ years in data/AI/analytics/enterprise architecture roles, including at least 35 years in presales or customerfacing solutioning roles.
• Proven track record designing and selling AI/ML or GenAI solutions for enterprise customers (ideally across multiple industries).
• Handson exposure to one or more major cloud platforms (AWS, Azure, GCP) and their AI/ML/GenAI services.
• Experience working closely with sales, bid teams, and senior client stakeholders in complex deals.
• Background in consulting, systems integration, or product companies with AI/analytics offerings is highly desirable.

Technical skills

• Strong understanding of AI/ML concepts: supervised/unsupervised learning, recommendation systems, NLP, computer vision, timeseries, etc.
• Solid knowledge of Generative AI/LLMs (prompting, finetuning, RAG, vector databases, safety and guardrails).
• Familiarity with modern data and AI stacks: data lakes/warehouses, ETL/ELT, streaming, MLOps/LLMOps, APIs, and microservices.
• Ability to design secure, scalable architectures integrating with enterprise systems (ERP/CRM/core systems, APIs, identity, monitoring).
• Understanding of AI governance, model lifecycle management, explainability, and responsible AI practices.

Non-technical skills

• Strong ability to translate business problems into AI solutions with clear value propositions and ROI narratives.
• Experience building business cases, TCO/ROI analyses, and phased roadmaps for AI adoption.
• Comfortable working in ambiguous environments and shaping earlystage opportunities into structured engagements.
• Strategic mindset with the ability to align solution designs to the AI CoE and enterprise strategy.
• Ownership and accountability for solution quality and customer success during presales.
• Collaborative working style; able to influence without direct authority across sales, delivery, and product/platform teams.
• Continuous learning attitude, staying updated with AI trends, tools, and best practices.

Preferred Qualifications

• Bachelors or Masters degree in Computer Science, Engineering, or related field.
• Certifications in cloud (AWS/Azure/GCP architect), data/AI/ML, or GenAI platforms.
• Certifications or training related to enterprise/solution architecture (e.g., TOGAF) are an added advantage.