Director of Software Engineering - Agentic AI
Salary not listed • Columbus, OH, United States • Full-time
Posted 4d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
This role is a hands-on leadership opportunity to shape the strategy and technical direction for AI-enabled delivery within a modern data platform. You will lead the design, delivery, and scaling of production-grade AI agent capabilities, taking agentic workflows from early results to scaled, production-grade capabilities. The goal is to improve speed, quality, and governance across data product delivery, leveraging a Databricks-based data platform on Amazon Web Services. You will partner closely with various teams including Information Architecture, Data Engineering, Data Governance, and business stakeholders to drive measurable outcomes.
Responsibilities
- Define and own the Applied AI roadmap for the data platform, translating business needs into capabilities that reduce manual effort and accelerate data product delivery.
- Architect and scale agent-driven workflows to improve data modeling, pipeline generation, data quality, and data classification with clear production readiness standards.
- Design and oversee a secure tool-integration layer that exposes platform capabilities to agents with controlled access and strong auditability.
- Establish technical standards for agent interaction with Databricks workspaces, Unity Catalog, Delta Lake tables, and policy controls for consistent and secure execution.
- Ensure performance, scalability, reliability, and operational readiness of AI-powered pipelines and tools, including monitoring, incident readiness, and continuous improvement.
- Build and run a human-in-the-loop review model for validating, versioning, tracing, and approving agent outputs before production use.
- Lead delivery across multiple concurrent workstreams, aligning cross-functional partners, managing dependencies, and communicating progress and risks to senior stakeholders.
- Hire, develop, and retain a high-performing team of AI engineers, data engineers, and platform specialists, fostering an inclusive culture.
- Set direction and governance for agentic AI-enabled engineering and Software Development Life Cycle automation to drive improvements in speed, quality, and operational outcomes.
- Apply knowledge of Software Development Life Cycle tools, including enterprise-authorized AI-assisted development and automation capabilities, to improve value realization and support capacity unlock initiatives.
Requirements
- Formal training or certification on software engineering concepts and 10+ years of applied experience.
- 5+ years of experience leading technologists to manage, anticipate, and solve complex technical items within your domain of expertise.
- Demonstrated experience leading engineering teams and owning technical roadmaps for platform capabilities used by multiple product teams.
- Deep hands-on expertise with Databricks, including Unity Catalog, Delta Lake, Apache Spark, and SQL Warehouses, with the ability to set secure, scalable patterns.
- Strong expertise in large language model application architecture, including RAG, tool integration, agent design patterns, and multi-agent orchestration with an emphasis on reliability and safety.
- Proven experience building, deploying, and operating production AI-enabled systems with strong engineering discipline (testing, observability, change management, and resiliency).
- Strong understanding of data governance and access control, including policy enforcement platforms and auditability expectations.
- Proven ability to communicate complex technical approaches as clear business value for senior stakeholders, including outcomes, trade-offs, risks, and investment needs.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment and ability to critically evaluate and validate AI-generated outputs.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.