AI Pre-sales Engineer

Key Responsibilities

• Client Discovery & Technical Engagement: Partner with Sales to lead technical discussions, scope customer challenges, and demonstrate how our AI solutions solve real-world problems.

• End-to-End Solution Architecture: Design practical full-stack AI solutions, selecting the right hardware sizing (GPUs/servers), AI/LLM models, and application workflows for the client's needs.

• Technical Scoping & SOW Generation: Translate messy client requirements into precise Technical Scopes of Work (SOW), defining realistic timelines, deliverables, and engineering milestones.

• Cross-Functional Translation: Serve as the critical bridge between external clients, commercial sales, and internal AI/software engineering teams to ensure aligned expectations and successful rollouts.

• Client Presentations: Deliver persuasive technical presentations and solution walkthroughs tailored for both business managers and technical evaluators.

• Forward Deployed Engineering & Integration: Jump into client environments to architect, deploy, and customize AI solutions, including RAG pipelines and autonomous agents via MCP.

Requirements

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field.

• Experience: 1–3+ years in a pre-sales, technical consulting, or solutions role. (Motivated fresh graduates with strong communication skills and AI fundamentals will be considered.)

• Full-Stack AI Awareness: Solid conceptual grasp of Generative AI, LLM deployment, application workflows, and the baseline hardware (GPUs, CPUs, storage) required to run them.

• Communication Skills: Exceptional ability to translate complex technical architectures into simple, value-driven explanations for non-technical stakeholders.

• SDLC & Estimations: Basic understanding of software lifecycles and deployment processes to accurately estimate delivery effort and collaborate with engineering teams.