• Map each prospect's ATS, scheduling tools, competency models, and hiring workflows to identify integration requirements and recommendations. • Build tailored demo flows that translate technical configurations into clear business value and help prospects understand how the product works in their specific context. • Serve as the primary implementation lead to ensure successful POCs. • Own first-pass RFP responses in partnership with AEs, standardize high-quality responses for reuse. • Build reusable materials, including integration explainers, demo templates, architecture diagrams, FAQs, etc. • Ensure seamless handoffs from pre- to post-sales for enterprise-scale customers.

• Serve as the trusted technical advisor throughout the sales cycle, driving new business and expansion, leading customer technical engagements, such as demos, workshops, and pilots, to ensure seamless solution alignment from pre-sales to post-sales. • Lead technical discovery to assess enterprises of 5,000+ employees’ environments, identify gaps, and define success criteria that guide proposal design and accelerate deals. • Maintain awareness of the AI, DSPM, and data security marketspace, understanding how evolving AI capabilities support customer objectives, and clearly articulate the resulting business impact to technical and executive stakeholders. • Enable, support, and train partners on Qohash solutions to drive new revenue through partner-led and co-sell motions. • Collaborate cross-functionally to support RFP responses, revenue-generating initiatives, and strategic expansion plans for existing accounts by defining additional use cases and value opportunities as a technical expert. • Maintain deep knowledge of the competitive landscape, handle technical objections with credibility, and act as the voice of the customer internally, channeling product feedback, feature requests, and technical gaps to your teammates. • Develop technical narratives and demo environments that connect revenue conversations to real-world outcomes, aligned to regional requirements, training needs, and priority use cases, keeping pace with evolving product capabilities and roadmap. • Build and scale the US technical sales engineering function as Qohash grows. • Travel nation-wide to meet business requirements, i.e., attend in-person meetings, conferences, and trade shows, or support strategic opportunities as needed.

• Collaborate closely with customers to understand their unique business needs and create, prototype, and deploy tailored solutions that align with their requirements. • Lead proof of concept evaluations to showcase the value of Clockwork's solutions within customer environments. • Provide technical leadership during customer meetings, presentations, and workshops, addressing any technical queries or concerns that arise. • Work closely with Clockwork's sales team to complete PoC evaluations and close deals. • Collaborate with product and engineering teams, and identify opportunities for product enhancement. • Offer valuable insights on product features, functionality, and performance, contributing to discussions about product strategy and architecture. • Stay abreast of the latest developments and trends in AI networking, Kubernetes, and cloud computing, sharing your thought leadership with customers and internal stakeholders. • Represent Clockwork at conferences and industry events, with occasional travel as required.

• Lead discovery sessions to identify high-value use cases for AI and automated solutions, including workflow automation, copilots, knowledge retrieval, and intelligent routing. • Translate business objectives into technical designs covering workflows, data flows, integration points, and non-functional requirements such as latency, reliability, security, and compliance. • Design and implement AI Agentic solutions that can reason, plan, call tools, and execute multi-step tasks across APIs, databases, SaaS platforms, and document repositories. • Build solution workflows using patterns such as planner-executor, supervisor-worker, and human-in-the-loop with frameworks including LangChain, LangGraph, and similar orchestration libraries such as LlamaIndex and Semantic Kernel. • Use MCP-style protocols to standardize tool discovery and invocation, including tool schemas, validation, retries, error handling, and safe execution patterns. • Implement retrieval-augmented generation pipelines, including ingestion, chunking, metadata design, embeddings, vector indexing, hybrid search, reranking, and retrieval orchestration. • Apply prompt and policy design techniques, including structured prompting, guarded templates, reasoning aids, and evaluation approaches to improve quality and reduce hallucinations. • Integrate AI and automated solutions into web and mobile apps, core enterprise systems (CRM, Support, ERP, HR, finance, ticketing, supply chain), collaboration tools, and process platforms.