Founding Senior Manager, Forward Deployed Engineering

Company Overview

The company develops an enterprise AI platform that helps grocery retailers improve decision-making across store operations, inventory management, and distribution networks. Its technology is designed for complex, rapidly changing environments, helping businesses optimize operations, reduce food waste, and improve efficiency throughout the supply chain. The organization is expanding its enterprise solutions and embedding AI into operational workflows at scale.

Responsibilities

• Build and lead the team: Hire, develop, and retain forward deployed engineers. Establish technical standards, customer engagement practices, and expectations for engineering quality.
• Manage customer engagements: Oversee multiple concurrent deployments, determining project priorities, delivery sequences, quality standards, and resource allocation.
• Develop engineering talent: Conduct regular one-on-one meetings, support career development, and help engineers strengthen their customer-facing and technical leadership skills.
• Provide technical guidance: Review architectures and code, lead technical discovery sessions, and collaborate with engineers on complex implementation and debugging challenges.
• Own technical customer relationships: Serve as the senior technical partner to customer engineering and data leaders, establish delivery roadmaps, communicate architectural decisions, and proactively address delivery risks.
• Coordinate account execution: Partner with deployment and account leaders to align technical plans with customer objectives, commercial considerations, and implementation timelines.
• Translate business goals into technical programs: Convert broad objectives into clearly scoped projects with defined deliverables, sequencing, costs, and deadlines.
• Understand enterprise operations: Investigate undocumented business processes, data definitions, approval workflows, and operational exceptions to ensure solutions reflect how customers actually work.
• Establish shared architecture: Develop and maintain a reference architecture that supports standard hosted deployments and customer-cloud implementations.
• Define platform boundaries: Determine which capabilities should become reusable platform features, which require customer-specific development, and which requests should not be pursued.
• Set production standards: Establish expectations for reliability, security reviews, data residency, evaluation, and tracing to verify that deployed AI systems perform as intended.
• Improve the platform through field experience: Collaborate with platform engineers to turn deployment insights into product improvements and reduce the effort required for subsequent implementations.
• Create repeatable delivery practices: Develop implementation playbooks, project-scoping templates, and starter repositories that capture lessons learned and standardize delivery.
• Travel for customer engagements: Visit customer sites for project kickoffs, implementation work, and progress reviews, with travel expected to account for approximately 25–40% of the role.

Qualifications

• 10+ years of experience in software engineering, solutions architecture, consulting, or technical customer-facing roles.
• At least 2 years of experience managing engineers in a services, post-sales, or forward deployed engineering organization.
• Strong executive communication skills, with the ability to discuss roadmaps with senior customer stakeholders and work through technical debugging with engineers.
• Broad architectural knowledge across data ingestion, data modeling, applications, and AI systems.
• Strong data skills, including the ability to evaluate unfamiliar enterprise data models, identify data quality risks, and plan implementations around imperfect data.
• Fluency in SQL and experience with a modern cloud data platform such as Databricks, BigQuery, or Snowflake.
• A track record of understanding complex business operations, including undocumented workflows, informal practices, and operational constraints.
• Production experience with AI and large language models, including retrieval, tool use, and agentic workflows.
• Experience evaluating AI system quality through measurable criteria rather than relying solely on subjective assessment.

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