Pre Sales AI Architect

Job Description Role Overview

As a Pre-Sales AI Architect, you will play a pivotal role in identifying, shaping, and securing new AI opportunities by bridging the gap between complex customer challenges and transformative, AI-powered solutions. A core part of this role is leading high-quality bid responses for tenders seeking AI solution partners, ensuring our proposals are compelling, technically robust, and commercially sound. You will also be responsible for scoping AI delivery work, defining effort, approach, and activities that set projects up for success.

This highly consultative role requires a strong blend of technical expertise, business acumen, and written communication excellence. You will work closely with clients, account teams, and delivery leads to understanding customer needs, craft persuasive AI solution narratives, and differentiate our offering during competitive tenders.

You are expected to act as a trusted advisor to both internal teams and client stakeholders, translating real-world challenges into scalable AI architectures that demonstrate tangible business value. Your work will directly support sales growth by shaping solution designs, estimating effort and risk, producing high-quality tender responses, and ensuring our proposals are both innovative and deliverable.

1. Shape and Scope AI Solutions in Response to Client Needs

• Engage with client stakeholders to uncover pain points, business drivers, and opportunities for AI-powered transformation.
• Conduct discovery sessions and assessments to define business requirements and align them with AI use cases.
• Lead early scoping activities, including defining delivery phases, resource profiles, timelines, assumptions, and risks for proposed AI projects.

2. Design and Propose AI Architectures

• Translate client requirements into scalable, cloud-based AI architectures that balance technical feasibility, business value, and delivery risk.
• Propose end-to-end AI/ML solutions—including model design, data pipelines, MLOps, and platform integration—tailored to each opportunity.
• Collaborate with delivery and engineering leads to validate feasibility, identify dependencies, and align design with execution capability.

3. Write and Lead AI Bid Responses

• Own the technical response for AI-related RFPs, RFIs, and procurement tenders, including
  • Detailed solution overviews and architecture descriptions
  • Technical narratives and approach justification
  • Delivery methodology and project structure
  • Clear scope, assumptions, estimates, and risk management
• Craft persuasive, well-structured bid content that highlights our expertise, solution differentiation, and alignment with tender requirements.
• Act as the technical conscience of the bid, balancing ambition with deliverability.

4. Articulate Value, Outcomes, and Innovation

• Demonstrate how proposed AI solutions will improve business performance, reduce costs, or unlock new capabilities.
• Present complex technical solutions in simple, compelling terms to both technical and non-technical stakeholders.
• Highlight innovation opportunities and ensure proposals showcase thought leadership and credibility.

5. Track Industry Trends and Use Cases

• Stay ahead of market trends in Generative AI, Responsible AI, and emerging industry applications.
• Advise internal teams on how evolving technologies (e.g. vector databases, RAG pipelines, prompt engineering) can strengthen future bid responses and solution designs.

• Proven experience in pre-sales, tender response writing, or client-facing architecture roles involving AI, data science, or analytics.
• Demonstrated ability to write high-quality bid responses for complex technology or AI solution tenders.
• Strong capability to scope AI/ML delivery work, including estimating effort, planning phases, and defining delivery models.
• Ability to elicit business needs and convert them into AI/ML solution designs in cloud environments (Azure, AWS, or GCP).
• Deep technical understanding of AI/ML concepts: supervised and unsupervised learning, GenAI, LLMs, embeddings, MLOps, vector search.
• Experience designing AI solutions using tools such as Azure ML, AWS Sage Maker, Google Vertex…