Deployment Strategist
Company Overview
Scrabble is mandated to hire a Deployment Strategist r for a fast growing AI infrastructure startup building enterprise grade AI systems in India.
Job Summary
The Deployment Strategist will serve as a critical bridge between customers, product, sales, and engineering teams. The role involves understanding customer workflows, identifying AI opportunities, and guiding solutions from discovery through pilot, deployment, and expansion. The strategist will own customer outcomes, ensure successful AI integrations, and contribute to shaping the company's product roadmap based on customer insights.
Responsibilities
- Engage directly with customers to understand their workflows, pain points, decision-makers, and success metrics.
- Identify and prioritize high-volume, repeatable use cases that deliver measurable improvements in quality or cost.
- Translate ambiguous business problems into clear use-case definitions, success metrics, and technical requirements for engineering teams.
- Conduct discovery sessions, demos, and walkthroughs with both business and technical stakeholders.
- Define and scope pilots, including workflow design, data requirements, evaluation criteria, timelines, and commercial terms.
- Lead the transition of successful pilots into full-scale production deployment, coordinating between customer teams and engineers.
- Collaborate with customers and engineering teams to establish evaluations measuring model quality, latency, reliability, and cost.
- Build business cases linking deployment results to key customer outcomes and value.
- Manage commercial negotiations, including proposals, pricing, and expansion opportunities.
- Provide feedback from customer deployments to influence product development, research, and model improvements.
- Track adoption, measure business outcomes, and support ongoing customer success as usage scales.
- Develop reusable playbooks, demos, case studies, and sales materials based on deployment learnings.
- Act as the primary owner of customer relationships, ensuring deployments meet or exceed expected business outcomes.
- Communicate technical trade-offs in accessible language to business leaders and align priorities with engineering teams.
Qualifications
- 2-3 years of experience in customer-facing roles such as consulting, product management, solutions or pre-sales, enterprise sales, or a founder's office, preferably in B2B technology.
- Working technical understanding of AI application components: APIs, prompting, retrieval (RAG), evaluations, and inference costs.
- Proven ability to convert ambiguous business problems into structured, prioritized solutions with clear success metrics.
- Hands-on experience with LLM products and building prototypes using no-code or low-code tools.
- Strong commercial instincts, including experience in discovery, business case development, and stakeholder management.
- Ability to manage projects from discovery through pilot and into full production.
- Excellent written and verbal communication skills, capable of engaging with executive leadership and technical teams.
- High ownership mindset and adaptability to fast-paced, ambiguous environments.
- Educational background in engineering, computer science, or related technical fields is preferred.
Preferred Skills
- Experience in pre-sales, solutions consulting, or customer success within AI, data, or developer products.
- Familiarity with LLM evaluation, Retrieval-Augmented Generation (RAG), agents, or fine-tuning.
- Understanding of inference economics, including cost per token, latency, and hosting trade-offs.
- Experience selling to or working with enterprise customers in consulting or product roles.
- Exposure to workflow automation, document processing, or enterprise integration projects.
- Knowledge of regulated industries such as banking, financial services, or insurance, especially regarding technology evaluation and procurement.
- An engineering degree or prior technical role experience is a plus.
