FDE/Deployment Strategist
Focus
Drive successful deployment and adoption of our InfiniteWatch AI-powered products by embedding with customers, translating ambiguous business problems into concrete technical solutions, and ensuring measurable impact. Act as the connective tissue between product, engineering, and end users to turn cutting-edge AI capabilities into real-world value.
Key Responsibilities
Product Deployment Ownership: Lead end-to-end deployment of InfiniteWatch products with strategic clients, ensuring fast time-to-value, reliability, and measurable business impact.
Client Embedding & Discovery: Work directly with client teams to deeply understand needs, data landscapes, and decision-making processes, translating real-world needs into deployable solutions.
Deployment Acceleration Tooling: Design and implement internal tools, frameworks, and repeatable playbooks that streamline onboarding, configuration, and rollout across customers.
Solution Configuration & Customization: Adapt InfiniteWatch capabilities to client-specific requirements through configuration, light development, and system integration,without fragmenting the core product.
Requirements → Platform Evolution: Identify recurring client needs and transform them into scalable platform features, influencing core product architecture and roadmap.
Technical & Product Translation: Act as the primary bridge between clients and engineering, converting business objectives into clear technical requirements and implementation plans.
Rapid Prototyping in Production Contexts: Build and validate prototypes, proofs-of-value, and pilots directly in client environments to de-risk deployments and guide product decisions.
Feedback Loops & Continuous Improvement: Capture structured insights from deployments and feed them back into product, engineering, and go-to-market teams to improve scalability and usability.
Adoption & Enablement: Support customer success through documentation, training, and best practices to ensure sustained adoption and long-term partnership.
Technical Stack
Core: Python • NodeJS • SQL • APIs • Modern Data Stacks (cloud warehouses, event streams)
AI Platforms: OpenAI • Anthropic • Gemini (hands-on usage, not just theory)
Deployment & Infrastructure: Cloud-native environments • CI/CD (working knowledge)
Productivity: Cursor / Claude (or similar)
Ideal Profile
Experience: 2–5+ years in top-tier consulting (McKinsey, BCG, Bain) or similar roles, with strong exposure to digital, data, or AI-driven transformations.
Technical Fluency: Comfortable working close to code and data: able to prototype, debug, and reason about AI systems without needing to be a pure ML engineer.
Business-to-Technical Translator: Exceptional at breaking down messy, high-level problems into structured solutions that engineers can build and customers can adopt.
Customer-Facing & Outcome-Driven: Thrives in high-ownership roles with direct customer exposure and accountability for real-world impact.
Bias for Action: Operates well in ambiguous environments, moves fast, and balances strategic thinking with hands-on execution.
Product-Minded Collaborator: Enjoys shaping product direction through real user needs and tight feedback loops with engineering and design.
Ready to Apply?
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