Agentic Context Engineer

Accountabilities: Lead technical pre-sales engagements in partnership with Sales, including discovery sessions, customer workshops, solution architecture, proof-of-concept development, and technical evaluations. Translate customer business challenges into valuable AI and analytics use cases, demonstrating technical feasibility and business impact. Own initial post-sales implementation from deal closure through the first production deployment, covering data onboarding, context modeling, validation, user enablement, and structured handover. Design and build scalable context and semantic models, agentic workflows, and integrations across complex enterprise data environments. Collaborate with customer data, IT, and security teams to resolve integration, performance, governance, security, and scalability challenges. Develop reusable engineering assets, including context models, certified demonstrations, solution patterns, and deployment playbooks, to improve delivery efficiency and consistency. Partner with Product and Engineering teams to translate customer requirements, technical gaps, and field insights into scalable platform improvements. Act as a trusted technical advisor to customer architects, data leaders, and executive stakeholders, connecting technical decisions with business outcomes. Requirements: 10+ years of experience in enterprise data, AI/ML, solutions engineering, or technical consulting, with a strong focus on delivering measurable business impact. Bachelor's or Master's degree in Engineering, Data, or a related discipline, or equivalent practical experience. Experience across both customer-facing pre-sales and post-sales technical engagements. Strong foundation in data architecture, data modeling, and scalable AI or analytics solutions, including experience working in complex enterprise environments. Hands-on experience with modern analytics and cloud data platforms, including semantic and metadata layers. Advanced SQL skills and strong Python proficiency for data transformation, orchestration, and automation. Understanding of Generative AI concepts and applications, including prompt engineering, Retrieval-Augmented Generation (RAG), and evaluation datasets. Familiarity with enterprise data governance, security, access controls, and performance optimization. Excellent communication and stakeholder management skills, with the ability to explain complex technical concepts and demonstrate business value to both technical and executive audiences. A strong sense of ownership, initiative, and accountability, with the ability to work independently and follow projects through to meaningful outcomes. Adaptability, sound judgment, and a collaborative approach to solving challenging customer problems. Benefits: Full-time, remote work arrangement, with San Francisco also listed as a location. Opportunity to work at the forefront of Generative AI and enterprise data technologies. High autonomy within a flat organizational structure, with the ability to influence product direction and business outcomes. Collaboration with an experienced engineering team with backgrounds at leading technology companies. Direct exposure to complex enterprise AI implementations and customer environments. Opportunity to develop reusable technical solutions and contribute to the evolution of an emerging AI platform. Salary, healthcare coverage, retirement benefits, paid leave, and other compensation details are not specified in the listing.